chore(deps-dev): update sqlalchemy-vertica-python requirement

Updates the requirements on [sqlalchemy-vertica-python](https://github.com/bluelabsio/sqlalchemy-vertica-python) to permit the latest version.
- [Release notes](https://github.com/bluelabsio/sqlalchemy-vertica-python/releases)
- [Commits](https://github.com/bluelabsio/sqlalchemy-vertica-python/compare/v0.5.9...v0.6.3)

---
updated-dependencies:
- dependency-name: sqlalchemy-vertica-python
  dependency-version: 0.6.3
  dependency-type: direct:development
...

Signed-off-by: dependabot[bot] <support@github.com>

.github/CODEOWNERS

@@ -36,6 +36,10 @@
 **/*.geojson @villebro @rusackas
 /superset-frontend/plugins/legacy-plugin-chart-country-map/ @villebro @rusackas
 
+# Notify translation maintainers of changes to translations
+
+/superset/translations/ @sfirke
+
 # Notify PMC members of changes to extension-related files
 
 /docs/developer_portal/extensions/ @michael-s-molina @villebro @rusackas

.github/workflows/ephemeral-env.yml

@@ -265,7 +265,7 @@ jobs:
 
       - name: Fill in the new image ID in the Amazon ECS task definition
         id: task-def
-        uses: aws-actions/amazon-ecs-render-task-definition@77954e213ba1f9f9cb016b86a1d4f6fcdea0d57e # v1
+        uses: aws-actions/amazon-ecs-render-task-definition@6853cfae8c3a7d978fbf68b5a55453395541dfbb # v1
         with:
           task-definition: .github/workflows/ecs-task-definition.json
           container-name: superset-ci
@@ -300,7 +300,7 @@ jobs:
           --tags key=pr,value=$PR_NUMBER key=github_user,value=${{ github.actor }}
       - name: Deploy Amazon ECS task definition
         id: deploy-task
-        uses: aws-actions/amazon-ecs-deploy-task-definition@fc8fc60f3a60ffd500fcb13b209c59d221ac8c8c # v2
+        uses: aws-actions/amazon-ecs-deploy-task-definition@a310a830f5c14e583e35d84e4e1ec7dd177c3c9c # v2
         with:
           task-definition: ${{ steps.task-def.outputs.task-definition }}
           service: pr-${{ github.event.inputs.issue_number || github.event.pull_request.number }}-service

docs/package.json

@@ -41,12 +41,12 @@
   },
   "dependencies": {
     "@ant-design/icons": "^6.2.2",
-    "@docusaurus/core": "^3.10.0",
-    "@docusaurus/faster": "^3.10.0",
-    "@docusaurus/plugin-client-redirects": "^3.10.0",
-    "@docusaurus/preset-classic": "3.10.0",
-    "@docusaurus/theme-live-codeblock": "^3.10.0",
-    "@docusaurus/theme-mermaid": "^3.10.0",
+    "@docusaurus/core": "^3.10.1",
+    "@docusaurus/faster": "^3.10.1",
+    "@docusaurus/plugin-client-redirects": "^3.10.1",
+    "@docusaurus/preset-classic": "3.10.1",
+    "@docusaurus/theme-live-codeblock": "^3.10.1",
+    "@docusaurus/theme-mermaid": "^3.10.1",
     "@emotion/core": "^11.0.0",
     "@emotion/react": "^11.13.3",
     "@emotion/styled": "^11.14.1",
@@ -92,8 +92,8 @@
     "unist-util-visit": "^5.1.0"
   },
   "devDependencies": {
-    "@docusaurus/module-type-aliases": "^3.10.0",
-    "@docusaurus/tsconfig": "^3.10.0",
+    "@docusaurus/module-type-aliases": "^3.10.1",
+    "@docusaurus/tsconfig": "^3.10.1",
     "@eslint/js": "^9.39.2",
     "@types/js-yaml": "^4.0.9",
     "@types/react": "^19.1.8",
@@ -124,8 +124,7 @@
   "resolutions": {
     "react-redux": "^9.2.0",
     "@reduxjs/toolkit": "^2.5.0",
-    "baseline-browser-mapping": "^2.9.19",
-    "webpackbar": "^7.0.0"
+    "baseline-browser-mapping": "^2.9.19"
   },
   "packageManager": "yarn@1.22.22+sha1.ac34549e6aa8e7ead463a7407e1c7390f61a6610"
 }

docs/yarn.lock

@@ -1570,10 +1570,10 @@
     "@docsearch/core" "4.6.2"
     "@docsearch/css" "4.6.2"
 
-"@docusaurus/babel@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/babel/-/babel-3.10.0.tgz#819819f107233dfcf50b59cd51158f23fb04878a"
-  integrity sha512-mqCJhCZNZUDg0zgDEaPTM4DnRsisa24HdqTy/qn/MQlbwhTb4WVaZg6ZyX6yIVKqTz8fS1hBMgM+98z+BeJJDg==
+"@docusaurus/babel@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/babel/-/babel-3.10.1.tgz#2f714f682117658ba43d308e9b35b6a73a105227"
+  integrity sha512-DZzFO1K3v/GoEt1fx1DiYHF4en+PuhtQf1AkQJa5zu3CoeKSpr5cpQRUlz3jr0m44wyzmSXu9bVpfir+N4+8bg==
   dependencies:
     "@babel/core" "^7.25.9"
     "@babel/generator" "^7.25.9"
@@ -1584,23 +1584,23 @@
     "@babel/preset-typescript" "^7.25.9"
     "@babel/runtime" "^7.25.9"
     "@babel/traverse" "^7.25.9"
-    "@docusaurus/logger" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
+    "@docusaurus/logger" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
     babel-plugin-dynamic-import-node "^2.3.3"
     fs-extra "^11.1.1"
     tslib "^2.6.0"
 
-"@docusaurus/bundler@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/bundler/-/bundler-3.10.0.tgz#878c4c46bfa3434671ea37a43da184238a6aae26"
-  integrity sha512-iONUGZGgp+lAkw/cJZH6irONcF4p8+278IsdRlq8lYhxGjkoNUs0w7F4gVXBYSNChq5KG5/JleTSsdJySShxow==
+"@docusaurus/bundler@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/bundler/-/bundler-3.10.1.tgz#82fa5079f3787a67502e25f82d37d05ec5de0cc3"
+  integrity sha512-HIqQPvbqnnQRe4NsBd1774KRarjXqS6wHsWELtyuSs1gCfvixJO2jUGH/OEBtr1Gvzpw+ze5CjGMvSJ8UE1KUw==
   dependencies:
     "@babel/core" "^7.25.9"
-    "@docusaurus/babel" "3.10.0"
-    "@docusaurus/cssnano-preset" "3.10.0"
-    "@docusaurus/logger" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
+    "@docusaurus/babel" "3.10.1"
+    "@docusaurus/cssnano-preset" "3.10.1"
+    "@docusaurus/logger" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
     babel-loader "^9.2.1"
     clean-css "^5.3.3"
     copy-webpack-plugin "^11.0.0"
@@ -1618,20 +1618,20 @@
     tslib "^2.6.0"
     url-loader "^4.1.1"
     webpack "^5.95.0"
-    webpackbar "^6.0.1"
+    webpackbar "^7.0.0"
 
-"@docusaurus/core@3.10.0", "@docusaurus/core@^3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/core/-/core-3.10.0.tgz#642e71a0209d62c3f5ef275ed9d74a881f40df39"
-  integrity sha512-mgLdQsO8xppnQZc3LPi+Mf+PkPeyxJeIx11AXAq/14fsaMefInQiMEZUUmrc7J+956G/f7MwE7tn8KZgi3iRcA==
+"@docusaurus/core@3.10.1", "@docusaurus/core@^3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/core/-/core-3.10.1.tgz#3f8bdb97451b4df14f2a3b39ab0186366fbf8fbe"
+  integrity sha512-3pf2fXXw0eVk8WnC3T4LIigRDupcpvngpKo9Vy7mYyBhuddc0klDUuZAIfzMoK6z05pdlk6EFC/vBSX43+1O5w==
   dependencies:
-    "@docusaurus/babel" "3.10.0"
-    "@docusaurus/bundler" "3.10.0"
-    "@docusaurus/logger" "3.10.0"
-    "@docusaurus/mdx-loader" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
-    "@docusaurus/utils-common" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/babel" "3.10.1"
+    "@docusaurus/bundler" "3.10.1"
+    "@docusaurus/logger" "3.10.1"
+    "@docusaurus/mdx-loader" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
+    "@docusaurus/utils-common" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     boxen "^6.2.1"
     chalk "^4.1.2"
     chokidar "^3.5.3"
@@ -1668,22 +1668,22 @@
     webpack-dev-server "^5.2.2"
     webpack-merge "^6.0.1"
 
-"@docusaurus/cssnano-preset@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/cssnano-preset/-/cssnano-preset-3.10.0.tgz#be1b435c33df09d743473d3fadda67b4568dfae3"
-  integrity sha512-qzSshTO1DB3TYW+dPUal5KHM7XPc5YQfzF3Kdb2NDACJUyGbNcFtw3tGkCJlYwhNCRKbZcmwraKUS1i5dcHdGg==
+"@docusaurus/cssnano-preset@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/cssnano-preset/-/cssnano-preset-3.10.1.tgz#4b6bafeca8bb9423364d2fd6683c28e2f85a4665"
+  integrity sha512-eNfHGcTKCSq6xmcavAkX3RRclHaE2xRCMParlDXLdXVP01/a2e/jKXMj/0ULnLFQSNwwuI62L0Ge8J+nZsR7UQ==
   dependencies:
     cssnano-preset-advanced "^6.1.2"
     postcss "^8.5.4"
     postcss-sort-media-queries "^5.2.0"
     tslib "^2.6.0"
 
-"@docusaurus/faster@^3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/faster/-/faster-3.10.0.tgz#0758a93196f685537aa7700bde62faf926e6c817"
-  integrity sha512-GNPtVH14ISjHfSwnHu3KiFGf86ICmJSQDeSv/QaanpBgiZGOtgZaslnC5q8WiguxM1EVkwcGxPuD8BXF4eggKw==
+"@docusaurus/faster@^3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/faster/-/faster-3.10.1.tgz#a63d89ae980c98e1eeab3ff15ee083f7c20ed353"
+  integrity sha512-XTZhE5C1gZ/DaYYMlSk02dwP5vhpQON5QHVz1s3892mSESAywgWanURpXEDAvt4GvGuq7s+XP8rTWHZvfaJmdQ==
   dependencies:
-    "@docusaurus/types" "3.10.0"
+    "@docusaurus/types" "3.10.1"
     "@rspack/core" "^1.7.10"
     "@swc/core" "^1.7.39"
     "@swc/html" "^1.13.5"
@@ -1694,22 +1694,22 @@
     tslib "^2.6.0"
     webpack "^5.95.0"
 
-"@docusaurus/logger@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/logger/-/logger-3.10.0.tgz#2bacbd004dd78e3da926dbe8f6fa9a930856575d"
-  integrity sha512-9jrZzFuBH1LDRlZ7cznAhCLmAZ3HSDqgwdrSSZdGHq9SPUOQgXXu8mnxe2ZRB9NS1PCpMTIOVUqDtZPIhMafZg==
+"@docusaurus/logger@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/logger/-/logger-3.10.1.tgz#34c964e32e18f120e30f80171a38cfefe72cfb4b"
+  integrity sha512-oPjNFnfJsRCkePVjkGrxWGq4MvJKRQT0r9jOP0eRBTZ7Wr9FAbzdP/Gjs0I2Ss6YRkPoEgygKG112OkE6skvJw==
   dependencies:
     chalk "^4.1.2"
     tslib "^2.6.0"
 
-"@docusaurus/mdx-loader@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/mdx-loader/-/mdx-loader-3.10.0.tgz#1d4b050d751389ecf38dee48bcb61e53df8ffb82"
-  integrity sha512-mQQV97080AH4PYNs087l202NMDqRopZA4mg5W76ZZyTFrmWhJ3mHg+8A+drJVENxw5/Q+wHMHLgsx+9z1nEs0A==
+"@docusaurus/mdx-loader@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/mdx-loader/-/mdx-loader-3.10.1.tgz#050ae9bc614158a4ec07a628aa75fa9ae90d7e82"
+  integrity sha512-GRmeb/wQ+iXRrFwcHBfgQhrJxGElgCsoTWZYDhccjsZVne1p8MK/EpQVIloXttz76TCe78kKD5AEG9n1xc1oxQ==
   dependencies:
-    "@docusaurus/logger" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/logger" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     "@mdx-js/mdx" "^3.0.0"
     "@slorber/remark-comment" "^1.0.0"
     escape-html "^1.0.3"
@@ -1732,12 +1732,12 @@
     vfile "^6.0.1"
     webpack "^5.88.1"
 
-"@docusaurus/module-type-aliases@3.10.0", "@docusaurus/module-type-aliases@^3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/module-type-aliases/-/module-type-aliases-3.10.0.tgz#749928f104d563f11f046bf0c9ab6489a470c7c8"
-  integrity sha512-/1O0Zg8w3DFrYX/I6Fbss7OJrtZw1QoyjDhegiFNHVi9A9Y0gQ3jUAytVxF6ywpAWpLyLxch8nN8H/V3XfzdJQ==
+"@docusaurus/module-type-aliases@3.10.1", "@docusaurus/module-type-aliases@^3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/module-type-aliases/-/module-type-aliases-3.10.1.tgz#22d39177c296786eb6e0d940699cd590cc93ca77"
+  integrity sha512-YoOZKUdGlp8xSYhuAkGdSo5Ydkbq4V4eK3sD8v0a2hloxCWdQbNBhkc+Ko9QyjpESc0BYcIGM5iHVAy5hdFV6w==
   dependencies:
-    "@docusaurus/types" "3.10.0"
+    "@docusaurus/types" "3.10.1"
     "@types/history" "^4.7.11"
     "@types/react" "*"
     "@types/react-router-config" "*"
@@ -1745,34 +1745,34 @@
     react-helmet-async "npm:@slorber/react-helmet-async@1.3.0"
     react-loadable "npm:@docusaurus/react-loadable@6.0.0"
 
-"@docusaurus/plugin-client-redirects@^3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-client-redirects/-/plugin-client-redirects-3.10.0.tgz#4dd4619817fd69462d1e6d986580343aeb911111"
-  integrity sha512-P+VLoLoZTc74so8+IbsaPZ33/mkf2BWL1CYXQpPRkl0v1QVCN2CgfsZY/8QtbYjQnx2upXUnv45abDhNcSggNw==
+"@docusaurus/plugin-client-redirects@^3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-client-redirects/-/plugin-client-redirects-3.10.1.tgz#e22ed20e5837b7c3a28258e3d1816c4239c82b36"
+  integrity sha512-LHgd+YDvkhfOHMAE6XtUng3DQNzVM765RqVRrMJgHtzAvfopQhY6ieprqjxDVBdv21cLma6I0jHr+YCZH8fL9A==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/logger" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
-    "@docusaurus/utils-common" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/logger" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
+    "@docusaurus/utils-common" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     eta "^2.2.0"
     fs-extra "^11.1.1"
     lodash "^4.17.21"
     tslib "^2.6.0"
 
-"@docusaurus/plugin-content-blog@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-content-blog/-/plugin-content-blog-3.10.0.tgz#10095291b637440847854ecb2c8afcd8746debd7"
-  integrity sha512-RuTz68DhB7CL96QO5UsFbciD7GPYq6QV+YMfF9V0+N4ZgLhJIBgpVAr8GobrKF6NRe5cyWWETU5z5T834piG9g==
+"@docusaurus/plugin-content-blog@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-content-blog/-/plugin-content-blog-3.10.1.tgz#0bd8de700ccbd8e95d920df2613304ef59abe72b"
+  integrity sha512-mmkgE6Q2+K74tnkou7tXlpDLvoCU/qkSa2GSQ3XUiHWvcebCoDQzS670RR3tO8PmaWlIyWWISYWzZLuMfxunRA==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/logger" "3.10.0"
-    "@docusaurus/mdx-loader" "3.10.0"
-    "@docusaurus/theme-common" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
-    "@docusaurus/utils-common" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/logger" "3.10.1"
+    "@docusaurus/mdx-loader" "3.10.1"
+    "@docusaurus/theme-common" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
+    "@docusaurus/utils-common" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     cheerio "1.0.0-rc.12"
     combine-promises "^1.1.0"
     feed "^4.2.2"
@@ -1785,20 +1785,20 @@
     utility-types "^3.10.0"
     webpack "^5.88.1"
 
-"@docusaurus/plugin-content-docs@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-content-docs/-/plugin-content-docs-3.10.0.tgz#9c4ea1d5a405340f28c281d2e4586c695a7c65a5"
-  integrity sha512-9BjHhf15ct8Z7TThTC0xRndKDVvMKmVsAGAN7W9FpNRzfMdScOGcXtLmcCWtJGvAezjOJIm6CxOYCy3Io5+RnQ==
+"@docusaurus/plugin-content-docs@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-content-docs/-/plugin-content-docs-3.10.1.tgz#261e0e982e4a937c05b462e3c5729374f433b752"
+  integrity sha512-2jRVrtzjf8LClGTHQlwlwuD3wQXRx3WEoF7XUarJ8Ou+0onV+SLtejsyfY9JLpfUh9hPhXM4pbBGkyAY4Bi3HQ==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/logger" "3.10.0"
-    "@docusaurus/mdx-loader" "3.10.0"
-    "@docusaurus/module-type-aliases" "3.10.0"
-    "@docusaurus/theme-common" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
-    "@docusaurus/utils-common" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/logger" "3.10.1"
+    "@docusaurus/mdx-loader" "3.10.1"
+    "@docusaurus/module-type-aliases" "3.10.1"
+    "@docusaurus/theme-common" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
+    "@docusaurus/utils-common" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     "@types/react-router-config" "^5.0.7"
     combine-promises "^1.1.0"
     fs-extra "^11.1.1"
@@ -1809,142 +1809,142 @@
     utility-types "^3.10.0"
     webpack "^5.88.1"
 
-"@docusaurus/plugin-content-pages@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-content-pages/-/plugin-content-pages-3.10.0.tgz#7670cbb3c849f434949f542bfdfded1580a13165"
-  integrity sha512-5amX8kEJI+nIGtuLVjYk59Y5utEJ3CHETFOPEE4cooIRLA4xM4iBsA6zFgu4ljcopeYwvBzFEWf5g2I6Yb9SkA==
+"@docusaurus/plugin-content-pages@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-content-pages/-/plugin-content-pages-3.10.1.tgz#8c6ffc2079ed0262548ecc4df1dea6add6aa9673"
+  integrity sha512-huJpaRPMl42nsFwuCXvV8bVDj2MazuwRJIUylI/RSlmZeJssVoZXeCjVf1y+1Drtpa9SKcdGn8yoJ76IRJijtw==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/mdx-loader" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/mdx-loader" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     fs-extra "^11.1.1"
     tslib "^2.6.0"
     webpack "^5.88.1"
 
-"@docusaurus/plugin-css-cascade-layers@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-css-cascade-layers/-/plugin-css-cascade-layers-3.10.0.tgz#71e318d842be95f92be6c3dca00ceea4971d0edb"
-  integrity sha512-6q1vtt5FJcg5osgkHeM1euErECNqEZ5Z1j69yiNx2luEBIso+nxCkS9nqj8w+MK5X7rvKEToGhFfOFWncs51pQ==
+"@docusaurus/plugin-css-cascade-layers@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-css-cascade-layers/-/plugin-css-cascade-layers-3.10.1.tgz#440578d95cbe1a6120936fa83df868d2626cd1d8"
+  integrity sha512-r//fn+MNHkE1wCof8T29VAQezt1enGCpsFxoziBbvLgBM4JfXN2P3rxrBaavHmvLvm7lYkpJeitcDthwnmWCTw==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     tslib "^2.6.0"
 
-"@docusaurus/plugin-debug@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-debug/-/plugin-debug-3.10.0.tgz#e77f924604e1e09d5d90fe0bdf23a3be8ea3307e"
-  integrity sha512-XcljKN+G+nmmK69uQA1d9BlYU3ZftG3T3zpK8/7Hf/wrOlV7TA4Ampdrdwkg0jElKdKAoSnPhCO0/U3bQGsVQQ==
+"@docusaurus/plugin-debug@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-debug/-/plugin-debug-3.10.1.tgz#b8b7b24d9a7d185fd8a56a030f90145d3bfd8239"
+  integrity sha512-9KqOpKNfAyqGZykRb9LhIT/vyRF6sm/ykhjj/39JvaJahDS+jZJE0Z1Wfz9q3DUNDTMNN0Q7u/kk4rKKU+IJuA==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
     fs-extra "^11.1.1"
     react-json-view-lite "^2.3.0"
     tslib "^2.6.0"
 
-"@docusaurus/plugin-google-analytics@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-google-analytics/-/plugin-google-analytics-3.10.0.tgz#22c7e976fe4d970c7cd1c73c9723d9a5786c6e37"
-  integrity sha512-hTEoodatpBZnUat5nFExbuTGA1lhWGy7vZGuTew5Q3QDtGKFpSJLYmZJhdTjvCFwv1+qQ67hgAVlKdJOB8TXow==
+"@docusaurus/plugin-google-analytics@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-google-analytics/-/plugin-google-analytics-3.10.1.tgz#ac15afc77386e0352edb8a1698d993aa5de36ffc"
+  integrity sha512-8o0P1KtmgdYQHH+oInitPpRWI0Of5XednAX4+DMhQNSmGSRNrsEEHg1ebv35m9AgRClfAytCJ5jA9KvcASTyuA==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     tslib "^2.6.0"
 
-"@docusaurus/plugin-google-gtag@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-google-gtag/-/plugin-google-gtag-3.10.0.tgz#c38a2ba638257851cc845b934506b80c08d47f96"
-  integrity sha512-iB/Zzjv/eelJRbdULZqzWCbgMgJ7ht4ONVjXtN3+BI/muil6S87gQ1OJyPwlXD+ELdKkitC7bWv5eJdYOZLhrQ==
+"@docusaurus/plugin-google-gtag@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-google-gtag/-/plugin-google-gtag-3.10.1.tgz#0482b83b9bc411aa99a432be2b39d2e53a00e2e0"
+  integrity sha512-pu3xIUo5o/zCMLfUY9BO5KOwSH0zIsAGyFRPvXHayFSA5XIhCU/SFuB0g0ZNjFn9niZLCaNvoeAuOGFJZq0fdw==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     "@types/gtag.js" "^0.0.20"
     tslib "^2.6.0"
 
-"@docusaurus/plugin-google-tag-manager@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-google-tag-manager/-/plugin-google-tag-manager-3.10.0.tgz#5469c923cc1ad4608399d0b17e5fcacd8e030d56"
-  integrity sha512-FEjZxqKgLHa+Wez/EgKxRwvArNCWIScfyEQD95rot7jkxp6nonjI5XIbGfO/iYhM5Qinwe8aIEQHP2KZtpqVuA==
+"@docusaurus/plugin-google-tag-manager@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-google-tag-manager/-/plugin-google-tag-manager-3.10.1.tgz#eaf5765d6f82b4fb661d92a793d1883f9d1ec106"
+  integrity sha512-f6fyGHiCm7kJHBtAisGQS5oNBnpnMTYQZxDXeVrnw/3zWU+LMA22pr6UHGYkBKDbN+qPC5QHG3NuOfzQLq3+Lw==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     tslib "^2.6.0"
 
-"@docusaurus/plugin-sitemap@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-sitemap/-/plugin-sitemap-3.10.0.tgz#35d59d46803f279f22aa64fc1bd18c048f12662b"
-  integrity sha512-DVTSLjB97hIjmayGnGcBfognCeI7ZuUKgEnU7Oz81JYqXtVg94mVTthDjq3QHTylYNeCUbkaW8VF0FDLcc8pPw==
+"@docusaurus/plugin-sitemap@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-sitemap/-/plugin-sitemap-3.10.1.tgz#66a6974bb2fd1b9d8f5cb0f3c5ecd2201c118565"
+  integrity sha512-C26MbmmqgdjkDq1htaZ3aD7LzEDKFWXfpyQpt0EOUThuq5nV77zDaedV20yHcVo9p+3ey9aZ4pbHA0D3QcZTzg==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/logger" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
-    "@docusaurus/utils-common" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/logger" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
+    "@docusaurus/utils-common" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     fs-extra "^11.1.1"
     sitemap "^7.1.1"
     tslib "^2.6.0"
 
-"@docusaurus/plugin-svgr@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-svgr/-/plugin-svgr-3.10.0.tgz#8ada2e6dd8318d20206a9b044fc091a5794ba3f0"
-  integrity sha512-lNljBESaETZqVBMPqkrGchr+UPT1eZzEPLmJhz8I76BxbjqgsUnRvrq6lQJ9sYjgmgX52KB7kkgczqd2yzoswQ==
+"@docusaurus/plugin-svgr@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/plugin-svgr/-/plugin-svgr-3.10.1.tgz#c217c24d6d23fd2bc6f54d44c040635b49d6b36e"
+  integrity sha512-6SFxsmjWFkVLDmBUvFK6i72QjUwqyQFe4Ovz+SUJophJjOyVG3ZZG5IQpBC/kX/Gfv1yWeU9nWauH6F6Q7QX/Q==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     "@svgr/core" "8.1.0"
     "@svgr/webpack" "^8.1.0"
     tslib "^2.6.0"
     webpack "^5.88.1"
 
-"@docusaurus/preset-classic@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/preset-classic/-/preset-classic-3.10.0.tgz#74b6facdaf568bcd41ec90cae9aebb7ca0ac8619"
-  integrity sha512-kw/Ye02Hc6xP1OdTswy8yxQEHg0fdPpyWAQRxr5b2x3h7LlG2Zgbb5BDFROnXDDMpUxB7YejlocJIE5HIEfpNA==
+"@docusaurus/preset-classic@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/preset-classic/-/preset-classic-3.10.1.tgz#faf330d96aedc9083a59bec09d966ae4dfc8b2fb"
+  integrity sha512-YO/FL8v1zmbxoTso6mjMz/RDjhaTJxb1UpFFTDdY5847LLDCeyYiYlrhyTbgN1RIN3xnkLKZ9Lj1x8hUzI4JOg==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/plugin-content-blog" "3.10.0"
-    "@docusaurus/plugin-content-docs" "3.10.0"
-    "@docusaurus/plugin-content-pages" "3.10.0"
-    "@docusaurus/plugin-css-cascade-layers" "3.10.0"
-    "@docusaurus/plugin-debug" "3.10.0"
-    "@docusaurus/plugin-google-analytics" "3.10.0"
-    "@docusaurus/plugin-google-gtag" "3.10.0"
-    "@docusaurus/plugin-google-tag-manager" "3.10.0"
-    "@docusaurus/plugin-sitemap" "3.10.0"
-    "@docusaurus/plugin-svgr" "3.10.0"
-    "@docusaurus/theme-classic" "3.10.0"
-    "@docusaurus/theme-common" "3.10.0"
-    "@docusaurus/theme-search-algolia" "3.10.0"
-    "@docusaurus/types" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/plugin-content-blog" "3.10.1"
+    "@docusaurus/plugin-content-docs" "3.10.1"
+    "@docusaurus/plugin-content-pages" "3.10.1"
+    "@docusaurus/plugin-css-cascade-layers" "3.10.1"
+    "@docusaurus/plugin-debug" "3.10.1"
+    "@docusaurus/plugin-google-analytics" "3.10.1"
+    "@docusaurus/plugin-google-gtag" "3.10.1"
+    "@docusaurus/plugin-google-tag-manager" "3.10.1"
+    "@docusaurus/plugin-sitemap" "3.10.1"
+    "@docusaurus/plugin-svgr" "3.10.1"
+    "@docusaurus/theme-classic" "3.10.1"
+    "@docusaurus/theme-common" "3.10.1"
+    "@docusaurus/theme-search-algolia" "3.10.1"
+    "@docusaurus/types" "3.10.1"
 
-"@docusaurus/theme-classic@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/theme-classic/-/theme-classic-3.10.0.tgz#d937915c691189f27ced649c822994d839ea565b"
-  integrity sha512-9msCAsRdN+UG+RwPwCFb0uKy4tGoPh5YfBozXeGUtIeAgsMdn6f3G/oY861luZ3t8S2ET8S9Y/1GnpJAGWytww==
+"@docusaurus/theme-classic@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/theme-classic/-/theme-classic-3.10.1.tgz#deed8cf73cc0f56113e53775cbb3b168c3c61566"
+  integrity sha512-VU1RK0qb2pab0si4r7HFK37cYco8VzqLj3u1PspVipSr/z/GPVKHO4/HXbnePqHoWDk8urjyGSeatH0NIMBM1A==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/logger" "3.10.0"
-    "@docusaurus/mdx-loader" "3.10.0"
-    "@docusaurus/module-type-aliases" "3.10.0"
-    "@docusaurus/plugin-content-blog" "3.10.0"
-    "@docusaurus/plugin-content-docs" "3.10.0"
-    "@docusaurus/plugin-content-pages" "3.10.0"
-    "@docusaurus/theme-common" "3.10.0"
-    "@docusaurus/theme-translations" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
-    "@docusaurus/utils-common" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/logger" "3.10.1"
+    "@docusaurus/mdx-loader" "3.10.1"
+    "@docusaurus/module-type-aliases" "3.10.1"
+    "@docusaurus/plugin-content-blog" "3.10.1"
+    "@docusaurus/plugin-content-docs" "3.10.1"
+    "@docusaurus/plugin-content-pages" "3.10.1"
+    "@docusaurus/theme-common" "3.10.1"
+    "@docusaurus/theme-translations" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
+    "@docusaurus/utils-common" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     "@mdx-js/react" "^3.0.0"
     clsx "^2.0.0"
     copy-text-to-clipboard "^3.2.0"
@@ -1959,15 +1959,15 @@
     tslib "^2.6.0"
     utility-types "^3.10.0"
 
-"@docusaurus/theme-common@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/theme-common/-/theme-common-3.10.0.tgz#70b419ccfdf62f092299354a72d1692e81be597d"
-  integrity sha512-Dkp1YXKn16ByCJAdIjbDIOpVb4Z66MsVD694/ilX1vAAHaVEMrVsf/NPd9VgreyFx08rJ9GqV1MtzsbTcU73Kg==
+"@docusaurus/theme-common@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/theme-common/-/theme-common-3.10.1.tgz#cbfec82b1b107be5c229811ed9caae14a501361c"
+  integrity sha512-0YtmIeoNo1fIw65LO8+/1dPgmDV86UmhMkow37gzjytuiCSQm9xob6PJy0L4kuQEMTLfUOGvkXvZr7GPrHquMA==
   dependencies:
-    "@docusaurus/mdx-loader" "3.10.0"
-    "@docusaurus/module-type-aliases" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
-    "@docusaurus/utils-common" "3.10.0"
+    "@docusaurus/mdx-loader" "3.10.1"
+    "@docusaurus/module-type-aliases" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
+    "@docusaurus/utils-common" "3.10.1"
     "@types/history" "^4.7.11"
     "@types/react" "*"
     "@types/react-router-config" "*"
@@ -1977,48 +1977,48 @@
     tslib "^2.6.0"
     utility-types "^3.10.0"
 
-"@docusaurus/theme-live-codeblock@^3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/theme-live-codeblock/-/theme-live-codeblock-3.10.0.tgz#05a38c6bfac479fd698f18f27ca06ebb126633d9"
-  integrity sha512-1Ycxu0dBAhEXzXPQ1dQW01aY1MNi7TCTUOBtIF0GcNrQBFj74XxhDqv/T6GxYBsaN+6QnIDs1T+D43iV2/r2hQ==
+"@docusaurus/theme-live-codeblock@^3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/theme-live-codeblock/-/theme-live-codeblock-3.10.1.tgz#29e6ddee467d816205ad611fd7bf10f00db5bdef"
+  integrity sha512-MKG/0zreelS6YlupQAoKmS5nCw9RRKwDHihJg2FinsU1+rqbrOYNYVq//eQy+m649k9b8XCazEw9VUMTFhpCTg==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/theme-common" "3.10.0"
-    "@docusaurus/theme-translations" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/theme-common" "3.10.1"
+    "@docusaurus/theme-translations" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     "@philpl/buble" "^0.19.7"
     clsx "^2.0.0"
     fs-extra "^11.1.1"
     react-live "^4.1.6"
     tslib "^2.6.0"
 
-"@docusaurus/theme-mermaid@^3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/theme-mermaid/-/theme-mermaid-3.10.0.tgz#6581ccf16d27e4c02fe8c7cf15488862f27be9c8"
-  integrity sha512-Y2xrlwhIJ80oOZIO3PXL6A7J869splfcMI87E3NKpYsy3zJxOyV+BP1QMtGi59ajKgU868HPuyyn6J+6BZGOBg==
+"@docusaurus/theme-mermaid@^3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/theme-mermaid/-/theme-mermaid-3.10.1.tgz#dada9c50c780524d246906234ace8a35446f26fc"
+  integrity sha512-2gxpmln8Pc4EN1oWzshQEx2HTs67jk14v7MmgqGs8ZU7Nm8oihg+fTouof2u4vN8DtB3Fln4cDJu4UprSX1S3Q==
   dependencies:
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/module-type-aliases" "3.10.0"
-    "@docusaurus/theme-common" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/module-type-aliases" "3.10.1"
+    "@docusaurus/theme-common" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     mermaid ">=11.6.0"
     tslib "^2.6.0"
 
-"@docusaurus/theme-search-algolia@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/theme-search-algolia/-/theme-search-algolia-3.10.0.tgz#0ff57fe58db6abde8f5ad2877e459cd2fa6e7464"
-  integrity sha512-f5FPKI08e3JRG63vR/o4qeuUVHUHzFzM0nnF+AkB67soAZgNsKJRf2qmUZvlQkGwlV+QFkKe4D0ANMh1jToU3g==
+"@docusaurus/theme-search-algolia@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/theme-search-algolia/-/theme-search-algolia-3.10.1.tgz#6f422058711629ce8d7c2f17e1e54efa075c626e"
+  integrity sha512-OTaARARVZj2GvkJQjB+1jOIxntRaXea+G+fMsNqrZBAU1O1vJKDW22R7kECOHW27oJCLFN9HKaZeRrfAUyviug==
   dependencies:
     "@algolia/autocomplete-core" "^1.19.2"
     "@docsearch/react" "^3.9.0 || ^4.3.2"
-    "@docusaurus/core" "3.10.0"
-    "@docusaurus/logger" "3.10.0"
-    "@docusaurus/plugin-content-docs" "3.10.0"
-    "@docusaurus/theme-common" "3.10.0"
-    "@docusaurus/theme-translations" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
-    "@docusaurus/utils-validation" "3.10.0"
+    "@docusaurus/core" "3.10.1"
+    "@docusaurus/logger" "3.10.1"
+    "@docusaurus/plugin-content-docs" "3.10.1"
+    "@docusaurus/theme-common" "3.10.1"
+    "@docusaurus/theme-translations" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
+    "@docusaurus/utils-validation" "3.10.1"
     algoliasearch "^5.37.0"
     algoliasearch-helper "^3.26.0"
     clsx "^2.0.0"
@@ -2028,23 +2028,23 @@
     tslib "^2.6.0"
     utility-types "^3.10.0"
 
-"@docusaurus/theme-translations@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/theme-translations/-/theme-translations-3.10.0.tgz#8fdc23d29bd7f907db49c36cf65e2123d96be300"
-  integrity sha512-L9IbFLwTc5+XdgH45iQYufLn0SVZd6BUNelDbKIFlH+E4hhjuj/XHWAFMX/w2K59rfy8wak9McOaei7BSUfRPA==
+"@docusaurus/theme-translations@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/theme-translations/-/theme-translations-3.10.1.tgz#c3119a015652290eea560ca45ac775963d6eb75b"
+  integrity sha512-cLMyaKivjBVWKMJuWqyFVVgtqe8DPJNPkog0bn8W1MDVAKcPdxRFycBfC1We1RaNp7Rdk513bmtW78RR6OBxBw==
   dependencies:
     fs-extra "^11.1.1"
     tslib "^2.6.0"
 
-"@docusaurus/tsconfig@^3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/tsconfig/-/tsconfig-3.10.0.tgz#f40a57248828f0503a5f355cf30aa59941c9baaa"
-  integrity sha512-TXdC3WXuPrdQAexLvjUJfnYf3YKEgEqAs5nK0Q88pRBCW7t7oN4ILvWYb3A5Z1wlSXyXGWW/mCUmLEhdWsjnDQ==
+"@docusaurus/tsconfig@^3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/tsconfig/-/tsconfig-3.10.1.tgz#1db31b4a4a5c914bdffa80070a35b6365d34f2e8"
+  integrity sha512-rYvB7yqkdqWIpAbDzQljGfM4cDBkLTbhmagZBEcsyj6oPUsz47lmW2pYdN1j+7sGFgltbAmQH62xfbrij4Eh6Q==
 
-"@docusaurus/types@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/types/-/types-3.10.0.tgz#a69232bba74b738fcf4671fd5f0f079366dd3d13"
-  integrity sha512-F0dOt3FOoO20rRaFK7whGFQZ3ggyrWEdQc/c8/UiRuzhtg4y1w9FspXH5zpCT07uMnJKBPGh+qNazbNlCQqvSw==
+"@docusaurus/types@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/types/-/types-3.10.1.tgz#d42837938ae43ca2be0ca47e63e00476b5eb94be"
+  integrity sha512-XYMK8k1szDCFMw2V+Xyen0g7Kee1sP3dtFnl7vkGkZOkeAJ/oPDQPL8iz4HBKOo/cwU8QeV6onVjMqtP+tFzsw==
   dependencies:
     "@mdx-js/mdx" "^3.0.0"
     "@types/history" "^4.7.11"
@@ -2057,36 +2057,36 @@
     webpack "^5.95.0"
     webpack-merge "^5.9.0"
 
-"@docusaurus/utils-common@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/utils-common/-/utils-common-3.10.0.tgz#2a6dc76b312664fca7234d33607c085318ff1ae3"
-  integrity sha512-JyL7sb9QVDgYvudIS81Dv0lsWm7le0vGZSDwsztxWam1SPBqrnkvBy9UYL/amh6pbybkyYTd3CMTkO24oMlCSw==
+"@docusaurus/utils-common@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/utils-common/-/utils-common-3.10.1.tgz#6350b4898691e765de750f90eade0e0fa7902d99"
+  integrity sha512-5mFSgEADtnFxFH7RLw02QA5MpU5JVUCj0MPeIvi/aF4Fi45tQRIuTwXoXDqJ+1VfQJuYJGz3SI63wmGz4HvXzA==
   dependencies:
-    "@docusaurus/types" "3.10.0"
+    "@docusaurus/types" "3.10.1"
     tslib "^2.6.0"
 
-"@docusaurus/utils-validation@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/utils-validation/-/utils-validation-3.10.0.tgz#a2418d7f31980d991fd3a1f39c8aad8820b36812"
-  integrity sha512-c+6n2+ZPOJtWWc8Bb/EYdpSDfjYEScdCu9fB/SNjOmSCf1IdVnGf2T53o0tsz0gDRtCL90tifTL0JE/oMuP1Mw==
+"@docusaurus/utils-validation@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/utils-validation/-/utils-validation-3.10.1.tgz#ddbcce997a5506424cdd16abf6845cc51692acae"
+  integrity sha512-cRv1X69jwaWv47waglllgZVWzeBFLhl53XT/XED/83BerVBTC5FTP8WTcVl8Z6sZOegDSwitu/wpCSPCDOT6lg==
   dependencies:
-    "@docusaurus/logger" "3.10.0"
-    "@docusaurus/utils" "3.10.0"
-    "@docusaurus/utils-common" "3.10.0"
+    "@docusaurus/logger" "3.10.1"
+    "@docusaurus/utils" "3.10.1"
+    "@docusaurus/utils-common" "3.10.1"
     fs-extra "^11.2.0"
     joi "^17.9.2"
     js-yaml "^4.1.0"
     lodash "^4.17.21"
     tslib "^2.6.0"
 
-"@docusaurus/utils@3.10.0":
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/@docusaurus/utils/-/utils-3.10.0.tgz#ea7d7b0d325b60f728decc00bb3908d00ef86faf"
-  integrity sha512-T3B0WTigsIthe0D4LQa2k+7bJY+c3WS+Wq2JhcznOSpn1lSN64yNtHQXboCj3QnUs1EuAZszQG1SHKu5w5ZrlA==
+"@docusaurus/utils@3.10.1":
+  version "3.10.1"
+  resolved "https://registry.yarnpkg.com/@docusaurus/utils/-/utils-3.10.1.tgz#535968caa2c9bff69f997a081b98b95b3c5d3785"
+  integrity sha512-3ojeJry9xBYdJO6qoyyzqeJFSJBVx2mXhyDzSdjwL2+URFQMf+h25gG38iswGImicK0ELjTd1EL2xzk8hf3QPw==
   dependencies:
-    "@docusaurus/logger" "3.10.0"
-    "@docusaurus/types" "3.10.0"
-    "@docusaurus/utils-common" "3.10.0"
+    "@docusaurus/logger" "3.10.1"
+    "@docusaurus/types" "3.10.1"
+    "@docusaurus/utils-common" "3.10.1"
     escape-string-regexp "^4.0.0"
     execa "^5.1.1"
     file-loader "^6.2.0"
@@ -13328,7 +13328,7 @@ renderkid@^3.0.0:
 
 repeat-string@^1.5.2:
   version "1.6.1"
-  resolved "https://registry.npmjs.org/repeat-string/-/repeat-string-1.6.1.tgz"
+  resolved "https://registry.yarnpkg.com/repeat-string/-/repeat-string-1.6.1.tgz#8dcae470e1c88abc2d600fff4a776286da75e637"
   integrity sha512-PV0dzCYDNfRi1jCDbJzpW7jNNDRuCOG/jI5ctQcGKt/clZD+YcPS3yIlWuTJMmESC8aevCFmWJy5wjAFgNqN6w==
 
 require-directory@^2.1.1:
@@ -15368,7 +15368,7 @@ webpack@^5.106.2, webpack@^5.88.1, webpack@^5.95.0:
     watchpack "^2.5.1"
     webpack-sources "^3.3.4"
 
-webpackbar@^6.0.1, webpackbar@^7.0.0:
+webpackbar@^7.0.0:
   version "7.0.0"
   resolved "https://registry.yarnpkg.com/webpackbar/-/webpackbar-7.0.0.tgz#7228d32881af2392381b6514499ddea73cdf218a"
   integrity sha512-aS9soqSO2iCHgqHoCrj4LbfGQUboDCYJPSFOAchEK+9psIjNrfSWW4Y0YEz67MKURNvMmfo0ycOg9d/+OOf9/Q==

pyproject.toml

@@ -197,7 +197,7 @@ tdengine = [
 ]
 teradata = ["teradatasql>=16.20.0.23"]
 thumbnails = [] # deprecated, will be removed in 7.0
-vertica = ["sqlalchemy-vertica-python>=0.5.9, < 0.6"]
+vertica = ["sqlalchemy-vertica-python>= 0.5.9, < 0.7"]
 netezza = ["nzalchemy>=11.0.2"]
 starrocks = ["starrocks>=1.0.0"]
 doris = ["pydoris>=1.0.0, <2.0.0"]

superset-core/pyproject.toml

@@ -18,7 +18,7 @@
 
 [project]
 name = "apache-superset-core"
-version = "0.1.0rc2"
+version = "0.1.0rc3"
 description = "Core Python package for building Apache Superset backend extensions and integrations"
 readme = "README.md"
 authors = [

superset-extensions-cli/pyproject.toml

@@ -17,7 +17,7 @@
 
 [project]
 name = "apache-superset-extensions-cli"
-version = "0.1.0rc2"
+version = "0.1.0rc3"
 description = "Official command-line interface for building, bundling, and managing Apache Superset extensions"
 readme = "README.md"
 authors = [

superset-frontend/package-lock.json

@@ -102,7 +102,7 @@
         "json-bigint": "^1.0.0",
         "json-stringify-pretty-compact": "^2.0.0",
         "lodash": "^4.18.1",
-        "mapbox-gl": "^3.22.0",
+        "mapbox-gl": "^3.23.0",
         "markdown-to-jsx": "^9.7.16",
         "match-sorter": "^8.3.0",
         "memoize-one": "^5.2.1",
@@ -163,7 +163,7 @@
         "@babel/plugin-transform-export-namespace-from": "^7.27.1",
         "@babel/plugin-transform-modules-commonjs": "^7.28.6",
         "@babel/plugin-transform-runtime": "^7.29.0",
-        "@babel/preset-env": "^7.29.2",
+        "@babel/preset-env": "^7.29.3",
         "@babel/preset-react": "^7.28.5",
         "@babel/preset-typescript": "^7.28.5",
         "@babel/register": "^7.23.7",
@@ -225,7 +225,7 @@
         "babel-plugin-dynamic-import-node": "^2.3.3",
         "babel-plugin-jsx-remove-data-test-id": "^3.0.0",
         "babel-plugin-lodash": "^3.3.4",
-        "baseline-browser-mapping": "^2.10.21",
+        "baseline-browser-mapping": "^2.10.24",
         "cheerio": "1.2.0",
         "concurrently": "^9.2.1",
         "copy-webpack-plugin": "^14.0.0",
@@ -578,9 +578,9 @@
       }
     },
     "node_modules/@babel/compat-data": {
-      "version": "7.29.0",
-      "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.29.0.tgz",
-      "integrity": "sha512-T1NCJqT/j9+cn8fvkt7jtwbLBfLC/1y1c7NtCeXFRgzGTsafi68MRv8yzkYSapBnFA6L3U2VSc02ciDzoAJhJg==",
+      "version": "7.29.3",
+      "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.29.3.tgz",
+      "integrity": "sha512-LIVqM46zQWZhj17qA8wb4nW/ixr2y1Nw+r1etiAWgRM6U1IqP+LNhL1yg440jYZR72jCWcWbLWzIosH+uP1fqg==",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -1062,6 +1062,23 @@
         "@babel/core": "^7.0.0"
       }
     },
+    "node_modules/@babel/plugin-bugfix-safari-rest-destructuring-rhs-array": {
+      "version": "7.29.3",
+      "resolved": "https://registry.npmjs.org/@babel/plugin-bugfix-safari-rest-destructuring-rhs-array/-/plugin-bugfix-safari-rest-destructuring-rhs-array-7.29.3.tgz",
+      "integrity": "sha512-SRS46DFR4HqzUzCVgi90/xMoL+zeBDBvWdKYXSEzh79kXswNFEglUpMKxR04//dPqwYXWUBJ3mpUd933ru9Kmg==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/helper-plugin-utils": "^7.28.6",
+        "@babel/helper-skip-transparent-expression-wrappers": "^7.27.1"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      },
+      "peerDependencies": {
+        "@babel/core": "^7.0.0"
+      }
+    },
     "node_modules/@babel/plugin-bugfix-v8-spread-parameters-in-optional-chaining": {
       "version": "7.27.1",
       "resolved": "https://registry.npmjs.org/@babel/plugin-bugfix-v8-spread-parameters-in-optional-chaining/-/plugin-bugfix-v8-spread-parameters-in-optional-chaining-7.27.1.tgz",
@@ -2388,19 +2405,20 @@
       }
     },
     "node_modules/@babel/preset-env": {
-      "version": "7.29.2",
-      "resolved": "https://registry.npmjs.org/@babel/preset-env/-/preset-env-7.29.2.tgz",
-      "integrity": "sha512-DYD23veRYGvBFhcTY1iUvJnDNpuqNd/BzBwCvzOTKUnJjKg5kpUBh3/u9585Agdkgj+QuygG7jLfOPWMa2KVNw==",
+      "version": "7.29.3",
+      "resolved": "https://registry.npmjs.org/@babel/preset-env/-/preset-env-7.29.3.tgz",
+      "integrity": "sha512-ySZypNLAIH1ClygLDQzVMoGQRViATnkHkYYV6TcNDz+8+jwZCdsguGvsb3EY5d9wyWyhmF1iSuFM0Yh5XPnqSA==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@babel/compat-data": "^7.29.0",
+        "@babel/compat-data": "^7.29.3",
         "@babel/helper-compilation-targets": "^7.28.6",
         "@babel/helper-plugin-utils": "^7.28.6",
         "@babel/helper-validator-option": "^7.27.1",
         "@babel/plugin-bugfix-firefox-class-in-computed-class-key": "^7.28.5",
         "@babel/plugin-bugfix-safari-class-field-initializer-scope": "^7.27.1",
         "@babel/plugin-bugfix-safari-id-destructuring-collision-in-function-expression": "^7.27.1",
+        "@babel/plugin-bugfix-safari-rest-destructuring-rhs-array": "^7.29.3",
         "@babel/plugin-bugfix-v8-spread-parameters-in-optional-chaining": "^7.27.1",
         "@babel/plugin-bugfix-v8-static-class-fields-redefine-readonly": "^7.28.6",
         "@babel/plugin-proposal-private-property-in-object": "7.21.0-placeholder-for-preset-env.2",
@@ -18689,9 +18707,9 @@
       "license": "MIT"
     },
     "node_modules/baseline-browser-mapping": {
-      "version": "2.10.21",
-      "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.10.21.tgz",
-      "integrity": "sha512-Q+rUQ7Uz8AHM7DEaNdwvfFCTq7a43lNTzuS94eiWqwyxfV/wJv+oUivef51T91mmRY4d4A1u9rcSvkeufCVXlA==",
+      "version": "2.10.24",
+      "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.10.24.tgz",
+      "integrity": "sha512-I2NkZOOrj2XuguvWCK6OVh9GavsNjZjK908Rq3mIBK25+GD8vPX5w2WdxVqnQ7xx3SrZJiCiZFu+/Oz50oSYSA==",
       "dev": true,
       "license": "Apache-2.0",
       "bin": {
@@ -35939,9 +35957,9 @@
       "license": "MIT"
     },
     "node_modules/mapbox-gl": {
-      "version": "3.22.0",
-      "resolved": "https://registry.npmjs.org/mapbox-gl/-/mapbox-gl-3.22.0.tgz",
-      "integrity": "sha512-ZIpF+oAMcQoDlvABmiRkHoydyBR9zI6CyDeVRa2/iyua0/B2+rPuIzoCV/CgN14P5F0HVk53GIZw220WSqJPyA==",
+      "version": "3.23.0",
+      "resolved": "https://registry.npmjs.org/mapbox-gl/-/mapbox-gl-3.23.0.tgz",
+      "integrity": "sha512-zzjNAaMNvXnAVEUrYpOWmRVEBCIWgDAMLRPvSOoKY3smKvrINFVrRK/1jEpUDbEa7Ppf5Q/nwC6E07tz/i7IKw==",
       "license": "SEE LICENSE IN LICENSE.txt",
       "workspaces": [
         "src/style-spec",
@@ -51021,7 +51039,7 @@
       "dependencies": {
         "chalk": "^5.6.2",
         "lodash-es": "^4.18.1",
-        "yeoman-generator": "^8.1.2",
+        "yeoman-generator": "^8.2.2",
         "yosay": "^3.0.0"
       },
       "devDependencies": {
@@ -51395,12 +51413,12 @@
     },
     "packages/superset-core": {
       "name": "@apache-superset/core",
-      "version": "0.1.0-rc2",
+      "version": "0.1.0-rc3",
       "license": "Apache-2.0",
       "devDependencies": {
         "@babel/cli": "^7.28.6",
         "@babel/core": "^7.29.0",
-        "@babel/preset-env": "^7.29.2",
+        "@babel/preset-env": "^7.29.3",
         "@babel/preset-react": "^7.28.5",
         "@babel/preset-typescript": "^7.28.5",
         "@emotion/styled": "^11.14.1",
@@ -52666,25 +52684,6 @@
       "integrity": "sha512-lDB5YccMydFBtasVtxnZ3MRBHuaoE8GKsppq+EchKL2U4nK/DmEpPHNH8MZe5HkMtpSiTSOZwfN0tzYjO/lJEw==",
       "license": "ISC"
     },
-    "plugins/legacy-plugin-chart-map-box": {
-      "name": "@superset-ui/legacy-plugin-chart-map-box",
-      "version": "0.20.3",
-      "extraneous": true,
-      "license": "Apache-2.0",
-      "dependencies": {
-        "@math.gl/web-mercator": "^4.1.0",
-        "prop-types": "^15.8.1",
-        "react-map-gl": "^6.1.19",
-        "supercluster": "^8.0.1"
-      },
-      "peerDependencies": {
-        "@apache-superset/core": "*",
-        "@superset-ui/chart-controls": "*",
-        "@superset-ui/core": "*",
-        "mapbox-gl": "*",
-        "react": "^17.0.2"
-      }
-    },
     "plugins/legacy-plugin-chart-paired-t-test": {
       "name": "@superset-ui/legacy-plugin-chart-paired-t-test",
       "version": "0.20.3",
@@ -53062,7 +53061,7 @@
       "license": "Apache-2.0",
       "dependencies": {
         "@math.gl/web-mercator": "^4.1.0",
-        "mapbox-gl": "^3.22.0",
+        "mapbox-gl": "^3.23.0",
         "maplibre-gl": "^5.24.0",
         "react-map-gl": "^8.1.0",
         "supercluster": "^8.0.1"
@@ -53473,103 +53472,6 @@
       "version": "1.0.0",
       "extraneous": true,
       "license": "Apache-2.0"
-    },
-    "node_modules/mem-fs-editor/node_modules/array-differ": {
-      "version": "4.0.0",
-      "resolved": "https://registry.npmjs.org/array-differ/-/array-differ-4.0.0.tgz",
-      "integrity": "sha512-Q6VPTLMsmXZ47ENG3V+wQyZS1ZxXMxFyYzA+Z/GMrJ6yIutAIEf9wTyroTzmGjNfox9/h3GdGBCVh43GVFx4Uw==",
-      "license": "MIT",
-      "optional": true,
-      "peer": true,
-      "engines": {
-        "node": "^12.20.0 || ^14.13.1 || >=16.0.0"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/sindresorhus"
-      }
-    },
-    "node_modules/mem-fs-editor/node_modules/array-union": {
-      "version": "3.0.1",
-      "resolved": "https://registry.npmjs.org/array-union/-/array-union-3.0.1.tgz",
-      "integrity": "sha512-1OvF9IbWwaeiM9VhzYXVQacMibxpXOMYVNIvMtKRyX9SImBXpKcFr8XvFDeEslCyuH/t6KRt7HEO94AlP8Iatw==",
-      "license": "MIT",
-      "optional": true,
-      "peer": true,
-      "engines": {
-        "node": ">=12"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/sindresorhus"
-      }
-    },
-    "node_modules/mem-fs-editor/node_modules/globby": {
-      "version": "14.0.2",
-      "resolved": "https://registry.npmjs.org/globby/-/globby-14.0.2.tgz",
-      "integrity": "sha512-s3Fq41ZVh7vbbe2PN3nrW7yC7U7MFVc5c98/iTl9c2GawNMKx/J648KQRW6WKkuU8GIbbh2IXfIRQjOZnXcTnw==",
-      "license": "MIT",
-      "optional": true,
-      "peer": true,
-      "dependencies": {
-        "@sindresorhus/merge-streams": "^2.1.0",
-        "fast-glob": "^3.3.2",
-        "ignore": "^5.2.4",
-        "path-type": "^5.0.0",
-        "slash": "^5.1.0",
-        "unicorn-magic": "^0.1.0"
-      },
-      "engines": {
-        "node": ">=18"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/sindresorhus"
-      }
-    },
-    "node_modules/mem-fs-editor/node_modules/multimatch": {
-      "version": "7.0.0",
-      "resolved": "https://registry.npmjs.org/multimatch/-/multimatch-7.0.0.tgz",
-      "integrity": "sha512-SYU3HBAdF4psHEL/+jXDKHO95/m5P2RvboHT2Y0WtTttvJLP4H/2WS9WlQPFvF6C8d6SpLw8vjCnQOnVIVOSJQ==",
-      "license": "MIT",
-      "optional": true,
-      "peer": true,
-      "dependencies": {
-        "array-differ": "^4.0.0",
-        "array-union": "^3.0.1",
-        "minimatch": "^9.0.3"
-      },
-      "engines": {
-        "node": ">=18"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/sindresorhus"
-      }
-    },
-    "node_modules/mem-fs-editor/node_modules/path-type": {
-      "version": "5.0.0",
-      "resolved": "https://registry.npmjs.org/path-type/-/path-type-5.0.0.tgz",
-      "integrity": "sha512-5HviZNaZcfqP95rwpv+1HDgUamezbqdSYTyzjTvwtJSnIH+3vnbmWsItli8OFEndS984VT55M3jduxZbX351gg==",
-      "license": "MIT",
-      "optional": true,
-      "peer": true,
-      "engines": {
-        "node": ">=12"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/sindresorhus"
-      }
-    },
-    "node_modules/mem-fs-editor/node_modules/slash": {
-      "version": "5.1.0",
-      "resolved": "https://registry.npmjs.org/slash/-/slash-5.1.0.tgz",
-      "integrity": "sha512-ZA6oR3T/pEyuqwMgAKT0/hAv8oAXckzbkmR0UkUosQ+Mc4RxGoJkRmwHgHufaenlyAgE1Mxgpdcrf75y6XcnDg==",
-      "license": "MIT",
-      "optional": true,
-      "peer": true,
-      "engines": {
-        "node": ">=14.16"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/sindresorhus"
-      }
     }
   }
 }

superset-frontend/package.json

@@ -183,7 +183,7 @@
     "json-bigint": "^1.0.0",
     "json-stringify-pretty-compact": "^2.0.0",
     "lodash": "^4.18.1",
-    "mapbox-gl": "^3.22.0",
+    "mapbox-gl": "^3.23.0",
     "markdown-to-jsx": "^9.7.16",
     "match-sorter": "^8.3.0",
     "memoize-one": "^5.2.1",
@@ -244,7 +244,7 @@
     "@babel/plugin-transform-export-namespace-from": "^7.27.1",
     "@babel/plugin-transform-modules-commonjs": "^7.28.6",
     "@babel/plugin-transform-runtime": "^7.29.0",
-    "@babel/preset-env": "^7.29.2",
+    "@babel/preset-env": "^7.29.3",
     "@babel/preset-react": "^7.28.5",
     "@babel/preset-typescript": "^7.28.5",
     "@babel/register": "^7.23.7",
@@ -306,7 +306,7 @@
     "babel-plugin-dynamic-import-node": "^2.3.3",
     "babel-plugin-jsx-remove-data-test-id": "^3.0.0",
     "babel-plugin-lodash": "^3.3.4",
-    "baseline-browser-mapping": "^2.10.21",
+    "baseline-browser-mapping": "^2.10.24",
     "cheerio": "1.2.0",
     "concurrently": "^9.2.1",
     "copy-webpack-plugin": "^14.0.0",

superset-frontend/packages/generator-superset/package.json

@@ -37,7 +37,7 @@
     "cross-env": "^10.1.0",
     "fs-extra": "^11.3.4",
     "jest": "^30.3.0",
-    "yeoman-test": "^11.3.1"
+    "yeoman-test": "^11.4.2"
   },
   "engines": {
     "npm": ">= 4.0.0",

superset-frontend/packages/superset-core/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@apache-superset/core",
-  "version": "0.1.0-rc2",
+  "version": "0.1.0-rc3",
   "description": "This package contains UI elements, APIs, and utility functions used by Superset.",
   "sideEffects": false,
   "main": "lib/index.js",
@@ -75,7 +75,7 @@
   "devDependencies": {
     "@babel/cli": "^7.28.6",
     "@babel/core": "^7.29.0",
-    "@babel/preset-env": "^7.29.2",
+    "@babel/preset-env": "^7.29.3",
     "@babel/preset-react": "^7.28.5",
     "@babel/preset-typescript": "^7.28.5",
     "typescript": "^5.0.0",

superset-frontend/plugins/plugin-chart-point-cluster-map/package.json

@@ -27,7 +27,7 @@
   ],
   "dependencies": {
     "@math.gl/web-mercator": "^4.1.0",
-    "mapbox-gl": "^3.22.0",
+    "mapbox-gl": "^3.23.0",
     "maplibre-gl": "^5.24.0",
     "react-map-gl": "^8.1.0",
     "supercluster": "^8.0.1"

superset-frontend/plugins/plugin-chart-table/src/TableChart.tsx

@@ -50,6 +50,7 @@ import {
   getTimeFormatterForGranularity,
   BinaryQueryObjectFilterClause,
   extractTextFromHTML,
+  TimeGranularity,
 } from '@superset-ui/core';
 import {
   styled,
@@ -309,6 +310,67 @@ function SelectPageSize({
 const getNoResultsMessage = (filter: string) =>
   filter ? t('No matching records found') : t('No records found');
 
+/**
+ * Calculates the inclusive/exclusive temporal range for a bucket.
+ * standard SQL range pattern: [start, end)
+ */
+function getTimeRangeFromGranularity(
+  startTime: Date,
+  granularity: TimeGranularity,
+): [Date, Date] {
+  const time = startTime.getTime();
+  const date = startTime.getUTCDate();
+  const month = startTime.getUTCMonth();
+  const year = startTime.getUTCFullYear();
+
+  // Constants
+  const MS_IN_SECOND = 1000;
+  const MS_IN_MINUTE = 60 * MS_IN_SECOND;
+  const MS_IN_HOUR = 60 * MS_IN_MINUTE;
+
+  switch (granularity) {
+    case TimeGranularity.SECOND:
+      return [startTime, new Date(time + MS_IN_SECOND)];
+    case TimeGranularity.MINUTE:
+      return [startTime, new Date(time + MS_IN_MINUTE)];
+    case TimeGranularity.FIVE_MINUTES:
+      return [startTime, new Date(time + MS_IN_MINUTE * 5)];
+    case TimeGranularity.TEN_MINUTES:
+      return [startTime, new Date(time + MS_IN_MINUTE * 10)];
+    case TimeGranularity.FIFTEEN_MINUTES:
+      return [startTime, new Date(time + MS_IN_MINUTE * 15)];
+    case TimeGranularity.THIRTY_MINUTES:
+      return [startTime, new Date(time + MS_IN_MINUTE * 30)];
+    case TimeGranularity.HOUR:
+      return [startTime, new Date(time + MS_IN_HOUR)];
+    case TimeGranularity.DAY:
+    case TimeGranularity.DATE:
+      return [startTime, new Date(Date.UTC(year, month, date + 1))];
+    case TimeGranularity.WEEK:
+    case TimeGranularity.WEEK_STARTING_SUNDAY:
+    case TimeGranularity.WEEK_STARTING_MONDAY:
+      return [startTime, new Date(Date.UTC(year, month, date + 7))];
+    case TimeGranularity.WEEK_ENDING_SATURDAY:
+    case TimeGranularity.WEEK_ENDING_SUNDAY:
+      // Week-ending buckets are labeled by the bucket's final day.
+      return [
+        new Date(Date.UTC(year, month, date - 6)),
+        new Date(Date.UTC(year, month, date + 1)),
+      ];
+    case TimeGranularity.MONTH:
+      return [startTime, new Date(Date.UTC(year, month + 1, 1))];
+    case TimeGranularity.QUARTER:
+      return [
+        startTime,
+        new Date(Date.UTC(year, Math.floor(month / 3) * 3 + 3, 1)),
+      ];
+    case TimeGranularity.YEAR:
+      return [startTime, new Date(Date.UTC(year + 1, 0, 1))];
+    default:
+      return [startTime, new Date(Date.UTC(year, month, date + 1))];
+  }
+}
+
 export default function TableChart<D extends DataRecord = DataRecord>(
   props: TableChartTransformedProps<D> & {
     sticky?: DataTableProps<D>['sticky'];
@@ -471,7 +533,7 @@ export default function TableChart<D extends DataRecord = DataRecord>(
                     // so that cross-filters work on the receiving chart
                     const resolvedCol = columnLabelToNameMap[col] ?? col;
                     const val = ensureIsArray(updatedFilters?.[col]);
-                    if (!val.length)
+                    if (!val.length || val[0] === null || (val[0] instanceof DateWithFormatter && val[0].input === null))
                       return {
                         col: resolvedCol,
                         op: 'IS NULL' as const,
@@ -578,15 +640,49 @@ export default function TableChart<D extends DataRecord = DataRecord>(
         const drillToDetailFilters: BinaryQueryObjectFilterClause[] = [];
         filteredColumnsMeta.forEach(col => {
           if (!col.isMetric) {
-            let dataRecordValue = value[col.key];
-            dataRecordValue = extractTextFromHTML(dataRecordValue);
+            const dataRecordValue = value[col.key];
 
-            drillToDetailFilters.push({
-              col: col.key,
-              op: '==',
-              val: dataRecordValue as string | number | boolean,
-              formattedVal: formatColumnValue(col, dataRecordValue)[1],
-            });
+            // FIX: Explicitly handle NULL values for temporal and non-temporal columns
+            // DateWithFormatter objects wrap nulls, so we must check both
+            if (
+              dataRecordValue == null ||
+              (dataRecordValue instanceof DateWithFormatter && dataRecordValue.input == null)
+            ) {
+              drillToDetailFilters.push({
+                col: col.key,
+                op: 'IS NULL' as any,
+                val: null,
+              });
+
+            } else if (col.dataType === GenericDataType.Temporal && timeGrain) {
+              const startTime =
+                dataRecordValue instanceof Date
+                  ? dataRecordValue
+                  : new Date(dataRecordValue as string | number);
+
+              const [rangeStartTime, rangeEndTime] = getTimeRangeFromGranularity(
+                startTime,
+                timeGrain,
+              );
+              const timeRangeValue = `${rangeStartTime.toISOString()} : ${rangeEndTime.toISOString()}`;
+
+              drillToDetailFilters.push({
+                col: col.key,
+                op: 'TEMPORAL_RANGE',
+                val: timeRangeValue,
+                grain: timeGrain,
+                formattedVal: formatColumnValue(col, dataRecordValue)[1],
+              });
+            } else {
+              // Non-temporal columns use exact match
+              const sanitizedValue = extractTextFromHTML(dataRecordValue);
+              drillToDetailFilters.push({
+                col: col.key,
+                op: '==',
+                val: sanitizedValue as string | number | boolean,
+                formattedVal: formatColumnValue(col, sanitizedValue)[1],
+              });
+            }
           }
         });
         onContextMenu(clientX, clientY, {
@@ -600,7 +696,7 @@ export default function TableChart<D extends DataRecord = DataRecord>(
                 filters: [
                   {
                     col: cellPoint.key,
-                    op: '==',
+                    op: (cellPoint.value == null || (cellPoint.value instanceof DateWithFormatter && cellPoint.value.input == null) ? 'IS NULL' : '==') as any,
                     val: extractTextFromHTML(cellPoint.value),
                   },
                 ],
@@ -615,6 +711,7 @@ export default function TableChart<D extends DataRecord = DataRecord>(
     isRawRecords,
     filteredColumnsMeta,
     getCrossFilterDataMask,
+    timeGrain,
   ]);
 
   const getHeaderColumns = useCallback(

superset-frontend/plugins/plugin-chart-table/src/transformProps.ts

@@ -130,13 +130,12 @@ const processComparisonTotals = (
     Object.keys(totalRecord).forEach(key => {
       if (totalRecord[key] !== undefined && !key.includes(comparisonSuffix)) {
         transformedTotals[`Main ${key}`] =
-          parseInt(transformedTotals[`Main ${key}`]?.toString() || '0', 10) +
-          parseInt(totalRecord[key]?.toString() || '0', 10);
+          parseFloat(transformedTotals[`Main ${key}`]?.toString() || '0') +
+          parseFloat(totalRecord[key]?.toString() || '0');
         transformedTotals[`# ${key}`] =
-          parseInt(transformedTotals[`# ${key}`]?.toString() || '0', 10) +
-          parseInt(
+          parseFloat(transformedTotals[`# ${key}`]?.toString() || '0') +
+          parseFloat(
             totalRecord[`${key}__${comparisonSuffix}`]?.toString() || '0',
-            10,
           );
         const { valueDifference, percentDifferenceNum } = calculateDifferences(
           transformedTotals[`Main ${key}`] as number,

superset-frontend/plugins/plugin-chart-table/test/TableChart.test.tsx

@@ -2360,3 +2360,76 @@ describe('plugin-chart-table', () => {
     });
   });
 });
+
+/**
+ * DRILL-TO-DETAIL FIX VERIFICATION (#23847)
+ */
+describe('Drill-to-Detail Temporal Range Logic', () => {
+  const renderChartAndOpenContextMenu = (
+    timeGrain?: TimeGranularity,
+    timestampValue?: string | number | null,
+  ) => {
+    const onContextMenu = jest.fn();
+    const data = cloneDeep(testData.basic);
+
+    if (timestampValue !== undefined) {
+      data.queriesData[0].data[0].__timestamp = timestampValue;
+    }
+
+    const props = transformProps({
+      ...data,
+      rawFormData: {
+        ...data.rawFormData,
+        ...(timeGrain ? { time_grain_sqla: timeGrain } : {}),
+      },
+      hooks: { onAddFilter: jest.fn(), onContextMenu, setDataMask: jest.fn() },
+    });
+    render(<TableChart {...props} sticky={false} />);
+
+    const tbody = screen.getAllByRole('rowgroup')[1];
+    fireEvent.contextMenu(tbody.querySelectorAll('td')[0]);
+
+    const [, , { drillToDetail }] = onContextMenu.mock.calls[0];
+    return drillToDetail.find((f: any) => f.col === '__timestamp');
+  };
+
+  test('uses TEMPORAL_RANGE for monthly grain', () => {
+    const filter = renderChartAndOpenContextMenu(TimeGranularity.MONTH);
+
+    expect(filter.op).toBe('TEMPORAL_RANGE');
+    expect(filter.val).toContain(
+      '2020-01-01T12:34:56.000Z : 2020-02-01T00:00:00.000Z',
+    );
+  });
+
+  test('uses the full bucket for week ending sunday grain', () => {
+    const filter = renderChartAndOpenContextMenu(
+      TimeGranularity.WEEK_ENDING_SUNDAY,
+      '2020-01-05T00:00:00',
+    );
+
+    expect(filter.op).toBe('TEMPORAL_RANGE');
+    expect(filter.val).toBe(
+      '2019-12-30T00:00:00.000Z : 2020-01-06T00:00:00.000Z',
+    );
+  });
+
+  test('uses the full bucket for week ending saturday grain', () => {
+    const filter = renderChartAndOpenContextMenu(
+      TimeGranularity.WEEK_ENDING_SATURDAY,
+      '2020-01-04T00:00:00',
+    );
+
+    expect(filter.op).toBe('TEMPORAL_RANGE');
+    expect(filter.val).toBe(
+      '2019-12-29T00:00:00.000Z : 2020-01-05T00:00:00.000Z',
+    );
+  });
+
+  test('correctly handles NULL values by emitting IS NULL instead of 1970 timestamp', () => {
+    const filter = renderChartAndOpenContextMenu(TimeGranularity.MONTH, null);
+
+    expect(filter.op).toBe('IS NULL');
+    expect(filter.val).toBeNull();
+  });
+});

superset/db_engine_specs/base.py

@@ -590,7 +590,9 @@ class BaseEngineSpec:  # pylint: disable=too-many-public-methods
     # Driver-specific params to be included in the `get_oauth2_token` request body
     oauth2_additional_token_request_params: dict[str, Any] = {}
     # Driver-specific exception that should be mapped to OAuth2RedirectError
-    oauth2_exception = OAuth2RedirectError
+    oauth2_exception: type[Exception] | tuple[type[Exception], ...] = (
+        OAuth2RedirectError
+    )
 
     # Does the query id related to the connection?
     # The default value is True, which means that the query id is determined when

superset/db_engine_specs/gsheets.py

@@ -31,6 +31,7 @@ from marshmallow import fields, Schema
 from marshmallow.exceptions import ValidationError
 from requests import Session
 from shillelagh.adapters.api.gsheets.lib import SCOPES
+from shillelagh.exceptions import UnauthenticatedError
 from sqlalchemy.engine import create_engine
 from sqlalchemy.engine.reflection import Inspector
 from sqlalchemy.engine.url import URL
@@ -40,7 +41,7 @@ from superset.databases.schemas import encrypted_field_properties, EncryptedStri
 from superset.db_engine_specs.base import DatabaseCategory
 from superset.db_engine_specs.shillelagh import ShillelaghEngineSpec
 from superset.errors import ErrorLevel, SupersetError, SupersetErrorType
-from superset.exceptions import SupersetException
+from superset.exceptions import OAuth2TokenRefreshError, SupersetException
 from superset.utils import json
 from superset.utils.oauth2 import get_oauth2_access_token
 
@@ -151,6 +152,7 @@ class GSheetsEngineSpec(ShillelaghEngineSpec):
         "https://accounts.google.com/o/oauth2/v2/auth"
     )
     oauth2_token_request_uri = "https://oauth2.googleapis.com/token"  # noqa: S105
+    oauth2_exception = (UnauthenticatedError, OAuth2TokenRefreshError)
 
     @classmethod
     def get_oauth2_authorization_uri(

superset/mcp_service/app.py

@@ -62,6 +62,7 @@ Dataset Management:
 - list_datasets: List datasets with advanced filters (1-based pagination)
 - get_dataset_info: Get detailed dataset information by ID (includes columns/metrics)
 - create_virtual_dataset: Save a SQL query as a virtual dataset for charting
+- query_dataset: Query a dataset using its semantic layer (saved metrics, dimensions, filters) without needing a saved chart
 
 Chart Management:
 - list_charts: List charts with advanced filters (1-based pagination)
@@ -164,6 +165,17 @@ Use created_by_me for authorship, owned_by_me for edit ownership, or both
 together for the union. All flags can be combined with 'filters' but not
 with 'search'.
 
+To query a dataset's semantic layer (metrics, dimensions):
+1. list_datasets(request={{}}) -> find a dataset
+2. get_dataset_info(request={{"identifier": <id>}}) -> examine columns AND metrics
+3. query_dataset(request={{
+     "dataset_id": <id>,
+     "metrics": ["count", "avg_revenue"],
+     "columns": ["category"],
+     "time_range": "Last 7 days",
+     "row_limit": 100
+   }}) -> returns tabular data using saved metrics and dimensions
+
 To explore data with SQL:
 1. list_datasets(request={{}}) -> find a dataset and note its database_id
 2. execute_sql(request={{"database_id": <id>, "sql": "SELECT ..."}})
@@ -520,6 +532,7 @@ from superset.mcp_service.dataset.tool import (  # noqa: F401, E402
     create_virtual_dataset,
     get_dataset_info,
     list_datasets,
+    query_dataset,
 )
 from superset.mcp_service.explore.tool import (  # noqa: F401, E402
     generate_explore_link,

superset/mcp_service/auth.py

@@ -632,6 +632,15 @@ def mcp_auth_hook(tool_func: F) -> F:  # noqa: C901
         @functools.wraps(tool_func)
         def sync_wrapper(*args: Any, **kwargs: Any) -> Any:
             with _get_app_context_manager():
+                # Clear any stale thread-local SQLAlchemy session before user lookup.
+                # Thread pool workers reuse threads across requests; db.session is
+                # scoped by thread (not ContextVar), so a prior request's session may
+                # still be bound to a different tenant's DB engine. Removing it here
+                # ensures the next DB access creates a fresh session bound to the
+                # correct engine for the current request.
+                from superset.extensions import db
+
+                db.session.remove()
                 user = _setup_user_context()
 
                 # No Flask context - this is a FastMCP internal operation

superset/mcp_service/chart/tool/list_charts.py

@@ -70,6 +70,8 @@ SORTABLE_CHART_COLUMNS = [
     "created_on",
 ]
 
+_DEFAULT_LIST_CHARTS_REQUEST = ListChartsRequest()
+
 
 @tool(
     tags=["core"],
@@ -81,7 +83,8 @@ SORTABLE_CHART_COLUMNS = [
     ),
 )
 async def list_charts(
-    request: ListChartsRequest, ctx: Context
+    request: ListChartsRequest | None = None,
+    ctx: Context = None,
 ) -> ChartList | ChartError:
     """List charts with filtering and search.
 
@@ -91,6 +94,7 @@ async def list_charts(
     Sortable columns for order_column: id, slice_name, viz_type, description,
     changed_on, created_on
     """
+    request = request or _DEFAULT_LIST_CHARTS_REQUEST.model_copy(deep=True)
     await ctx.info(
         "Listing charts: page=%s, page_size=%s, search=%s"
         % (

superset/mcp_service/dashboard/tool/list_dashboards.py

@@ -65,6 +65,8 @@ SORTABLE_DASHBOARD_COLUMNS = [
     "created_on",
 ]
 
+_DEFAULT_LIST_DASHBOARDS_REQUEST = ListDashboardsRequest()
+
 
 @tool(
     tags=["core"],
@@ -76,7 +78,8 @@ SORTABLE_DASHBOARD_COLUMNS = [
     ),
 )
 async def list_dashboards(
-    request: ListDashboardsRequest, ctx: Context
+    request: ListDashboardsRequest | None = None,
+    ctx: Context = None,
 ) -> DashboardList:
     """List dashboards with filtering and search. Returns dashboard metadata
     including title, slug, URL, and last modified time. Use select_columns to
@@ -85,6 +88,7 @@ async def list_dashboards(
     Sortable columns for order_column: id, dashboard_title, slug, published,
     changed_on, created_on
     """
+    request = request or _DEFAULT_LIST_DASHBOARDS_REQUEST.model_copy(deep=True)
     await ctx.info(
         "Listing dashboards: page=%s, page_size=%s, search=%s"
         % (

superset/mcp_service/dataset/schemas.py

@@ -36,10 +36,13 @@ from pydantic import (
 )
 
 from superset.daos.base import ColumnOperator, ColumnOperatorEnum
+from superset.mcp_service.chart.schemas import DataColumn, PerformanceMetadata
 from superset.mcp_service.common.cache_schemas import (
+    CacheStatus,
     CreatedByMeMixin,
     MetadataCacheControl,
     OwnedByMeMixin,
+    QueryCacheControl,
 )
 from superset.mcp_service.constants import DEFAULT_PAGE_SIZE, MAX_PAGE_SIZE
 from superset.mcp_service.privacy import filter_user_directory_fields
@@ -393,6 +396,146 @@ class CreateVirtualDatasetResponse(BaseModel):
     )
 
 
+VALID_FILTER_OPS = Literal[
+    "==",
+    "!=",
+    ">",
+    "<",
+    ">=",
+    "<=",
+    "LIKE",
+    "NOT LIKE",
+    "ILIKE",
+    "NOT ILIKE",
+    "IN",
+    "NOT IN",
+    "IS NULL",
+    "IS NOT NULL",
+    "IS TRUE",
+    "IS FALSE",
+    "TEMPORAL_RANGE",
+]
+
+
+class QueryDatasetFilter(BaseModel):
+    """A single filter condition for dataset queries."""
+
+    col: str = Field(..., description="Column name to filter on")
+    op: VALID_FILTER_OPS = Field(
+        ...,
+        description=(
+            'Filter operator. Use "==" for equals, "!=" for not equals, '
+            '"IN" / "NOT IN" for membership, "IS NULL" / "IS NOT NULL", '
+            '"LIKE" for pattern matching, "TEMPORAL_RANGE" for time filters.'
+        ),
+    )
+    val: Any = Field(
+        default=None,
+        description="Filter value (omit for IS NULL/IS NOT NULL)",
+    )
+
+
+class QueryDatasetRequest(QueryCacheControl):
+    """Request schema for query_dataset tool."""
+
+    dataset_id: int | str = Field(
+        ...,
+        description="Dataset identifier — numeric ID or UUID string.",
+    )
+    metrics: List[str] = Field(
+        default_factory=list,
+        description=(
+            "Saved metric names to compute (e.g. ['count', 'avg_revenue']). "
+            "Use get_dataset_info to discover available metrics."
+        ),
+    )
+    columns: List[str] = Field(
+        default_factory=list,
+        description=(
+            "Column/dimension names for GROUP BY or SELECT "
+            "(e.g. ['category', 'region']). "
+            "Use get_dataset_info to discover available columns."
+        ),
+    )
+    filters: List[QueryDatasetFilter] = Field(
+        default_factory=list,
+        description=(
+            'Filter conditions (e.g. [{"col": "status", "op": "==", "val": "active"}]).'
+        ),
+    )
+    time_range: str | None = Field(
+        default=None,
+        description=(
+            "Time range filter (e.g. 'Last 7 days', 'Last month', "
+            "'2024-01-01 : 2024-12-31'). Requires a temporal column "
+            "on the dataset."
+        ),
+    )
+    time_column: str | None = Field(
+        default=None,
+        description=(
+            "Temporal column to apply time_range to. "
+            "Defaults to the dataset's main datetime column."
+        ),
+    )
+    order_by: List[str] | None = Field(
+        default=None,
+        description="Column or metric names to sort results by.",
+    )
+    order_desc: bool = Field(
+        default=True,
+        description="Sort descending (True) or ascending (False).",
+    )
+    row_limit: int = Field(
+        default=1000,
+        ge=1,
+        le=50000,
+        description="Maximum number of rows to return (default 1000, max 50000).",
+    )
+
+    @model_validator(mode="after")
+    def validate_metrics_or_columns(self) -> "QueryDatasetRequest":
+        """At least one of metrics or columns must be provided."""
+        if not self.metrics and not self.columns:
+            raise ValueError(
+                "At least one of 'metrics' or 'columns' must be provided. "
+                "Use get_dataset_info to discover available metrics and columns."
+            )
+        return self
+
+
+class QueryDatasetResponse(BaseModel):
+    """Response schema for query_dataset tool."""
+
+    model_config = ConfigDict(ser_json_timedelta="iso8601")
+
+    dataset_id: int = Field(..., description="Dataset ID")
+    dataset_name: str = Field(..., description="Dataset name")
+    columns: List[DataColumn] = Field(
+        default_factory=list, description="Column metadata for returned data"
+    )
+    data: List[Dict[str, Any]] = Field(
+        default_factory=list, description="Query result rows"
+    )
+    row_count: int = Field(0, description="Number of rows returned")
+    total_rows: int | None = Field(
+        None, description="Total row count from the query engine"
+    )
+    summary: str = Field("", description="Human-readable summary of the results")
+    performance: PerformanceMetadata | None = Field(
+        None, description="Query performance metadata"
+    )
+    cache_status: CacheStatus | None = Field(
+        None, description="Cache hit/miss information"
+    )
+    applied_filters: List[QueryDatasetFilter] = Field(
+        default_factory=list, description="Filters that were applied to the query"
+    )
+    warnings: List[str] = Field(
+        default_factory=list, description="Any warnings encountered during execution"
+    )
+
+
 def _parse_json_field(obj: Any, field_name: str) -> Dict[str, Any] | None:
     """Parse a field that may be stored as a JSON string into a dict."""
     value = getattr(obj, field_name, None)

superset/mcp_service/dataset/tool/__init__.py

@@ -18,9 +18,11 @@
 from .create_virtual_dataset import create_virtual_dataset
 from .get_dataset_info import get_dataset_info
 from .list_datasets import list_datasets
+from .query_dataset import query_dataset
 
 __all__ = [
     "create_virtual_dataset",
     "list_datasets",
     "get_dataset_info",
+    "query_dataset",
 ]

superset/mcp_service/dataset/tool/query_dataset.py

@@ -0,0 +1,489 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+MCP tool: query_dataset
+
+Query a dataset using its semantic layer (saved metrics, calculated columns,
+dimensions) without requiring a saved chart.
+"""
+
+import difflib
+import logging
+import time
+from typing import Any
+
+from fastmcp import Context
+from sqlalchemy.exc import SQLAlchemyError
+from sqlalchemy.orm import joinedload, subqueryload
+from superset_core.mcp.decorators import tool, ToolAnnotations
+
+from superset.commands.exceptions import CommandException
+from superset.exceptions import OAuth2Error, OAuth2RedirectError, SupersetException
+from superset.extensions import event_logger
+from superset.mcp_service.chart.schemas import DataColumn, PerformanceMetadata
+from superset.mcp_service.dataset.schemas import (
+    DatasetError,
+    QueryDatasetFilter,
+    QueryDatasetRequest,
+    QueryDatasetResponse,
+)
+from superset.mcp_service.privacy import (
+    DATA_MODEL_METADATA_ERROR_TYPE,
+    requires_data_model_metadata_access,
+    user_can_view_data_model_metadata,
+)
+from superset.mcp_service.utils import _is_uuid
+from superset.mcp_service.utils.cache_utils import get_cache_status_from_result
+from superset.mcp_service.utils.oauth2_utils import build_oauth2_redirect_message
+
+logger = logging.getLogger(__name__)
+
+
+def _resolve_dataset(identifier: int | str, eager_options: list[Any]) -> Any | None:
+    """Resolve a dataset by int ID or UUID string.
+
+    Replicates the identifier resolution logic from ModelGetInfoCore._find_object().
+    """
+    from superset.daos.dataset import DatasetDAO
+
+    opts = eager_options or None
+
+    if isinstance(identifier, int):
+        return DatasetDAO.find_by_id(identifier, query_options=opts)
+
+    # Try parsing as int
+    try:
+        id_val = int(identifier)
+        return DatasetDAO.find_by_id(id_val, query_options=opts)
+    except (ValueError, TypeError):
+        pass
+
+    # Try UUID
+    if _is_uuid(str(identifier)):
+        return DatasetDAO.find_by_id(identifier, id_column="uuid", query_options=opts)
+
+    return None
+
+
+def _validate_names(
+    requested: list[str],
+    valid: set[str],
+    kind: str,
+) -> list[str]:
+    """Return list of error messages for names not found in *valid*.
+
+    Includes close-match suggestions when available.
+    """
+    errors: list[str] = []
+    for name in requested:
+        if name not in valid:
+            suggestions = difflib.get_close_matches(name, valid, n=3, cutoff=0.6)
+            msg = f"Unknown {kind}: '{name}'"
+            if suggestions:
+                msg += f". Did you mean: {', '.join(suggestions)}?"
+            errors.append(msg)
+    return errors
+
+
+@requires_data_model_metadata_access
+@tool(
+    tags=["data"],
+    class_permission_name="Dataset",
+    annotations=ToolAnnotations(
+        title="Query dataset",
+        readOnlyHint=True,
+        destructiveHint=False,
+    ),
+)
+async def query_dataset(  # noqa: C901
+    request: QueryDatasetRequest, ctx: Context
+) -> QueryDatasetResponse | DatasetError:
+    """Query a dataset using its semantic layer (saved metrics, dimensions, filters).
+
+    Returns tabular data without requiring a saved chart. Use this when you want
+    to compute saved metrics, group by dimensions, or apply filters directly
+    against a dataset's curated semantic layer.
+
+    Workflow:
+    1. list_datasets -> find a dataset
+    2. get_dataset_info -> discover available columns and metrics
+    3. query_dataset -> query using metric names and column names
+
+    Example:
+    ```json
+    {
+        "dataset_id": 123,
+        "metrics": ["count", "avg_revenue"],
+        "columns": ["product_category"],
+        "time_range": "Last 7 days",
+        "row_limit": 100
+    }
+    ```
+    """
+    await ctx.info(
+        "Starting dataset query: dataset_id=%s, metrics=%s, columns=%s, "
+        "row_limit=%s"
+        % (
+            request.dataset_id,
+            request.metrics,
+            request.columns,
+            request.row_limit,
+        )
+    )
+
+    try:
+        from superset.commands.chart.data.get_data_command import ChartDataCommand
+        from superset.common.query_context_factory import QueryContextFactory
+        from superset.connectors.sqla.models import SqlaTable
+
+        # ------------------------------------------------------------------
+        # Step 1: Check data-model metadata access BEFORE the dataset lookup.
+        # Doing this first prevents leaking dataset existence — restricted
+        # users always receive DataModelMetadataRestricted, never NotFound.
+        # The decorator hides this tool from search; this check enforces
+        # direct calls that bypass tool discovery.
+        # ------------------------------------------------------------------
+        if not user_can_view_data_model_metadata():
+            await ctx.warning("Dataset metadata access blocked by privacy controls")
+            return DatasetError.create(
+                error=(
+                    "You don't have permission to access dataset details for your role."
+                ),
+                error_type=DATA_MODEL_METADATA_ERROR_TYPE,
+            )
+
+        # ------------------------------------------------------------------
+        # Step 2: Resolve dataset
+        # ------------------------------------------------------------------
+        await ctx.report_progress(1, 5, "Looking up dataset")
+        eager_options = [
+            subqueryload(SqlaTable.columns),
+            subqueryload(SqlaTable.metrics),
+            joinedload(SqlaTable.database),
+        ]
+
+        with event_logger.log_context(action="mcp.query_dataset.lookup"):
+            dataset = _resolve_dataset(request.dataset_id, eager_options)
+
+        if dataset is None:
+            await ctx.error("Dataset not found: identifier=%s" % (request.dataset_id,))
+            return DatasetError.create(
+                error=f"No dataset found with identifier: {request.dataset_id}",
+                error_type="NotFound",
+            )
+
+        dataset_name = getattr(dataset, "table_name", None) or f"Dataset {dataset.id}"
+        await ctx.info(
+            "Dataset found: id=%s, name=%s, columns=%s, metrics=%s"
+            % (
+                dataset.id,
+                dataset_name,
+                len(dataset.columns),
+                len(dataset.metrics),
+            )
+        )
+
+        # ------------------------------------------------------------------
+        # Step 2: Validate requested columns and metrics
+        # ------------------------------------------------------------------
+        await ctx.report_progress(2, 5, "Validating columns and metrics")
+        valid_columns = {c.column_name for c in dataset.columns}
+        valid_metrics = {m.metric_name for m in dataset.metrics}
+
+        validation_errors: list[str] = []
+        validation_errors.extend(
+            _validate_names(request.columns, valid_columns, "column")
+        )
+        validation_errors.extend(
+            _validate_names(request.metrics, valid_metrics, "metric")
+        )
+        # Validate filter column names against dataset columns
+        filter_cols = [f.col for f in request.filters]
+        validation_errors.extend(
+            _validate_names(filter_cols, valid_columns, "filter column")
+        )
+        # Validate order_by names against columns + metrics
+        if request.order_by:
+            valid_orderby = valid_columns | valid_metrics
+            validation_errors.extend(
+                _validate_names(request.order_by, valid_orderby, "order_by")
+            )
+
+        if validation_errors:
+            error_msg = "; ".join(validation_errors)
+            await ctx.error("Validation failed: %s" % (error_msg,))
+            return DatasetError.create(
+                error=error_msg,
+                error_type="ValidationError",
+            )
+
+        # ------------------------------------------------------------------
+        # Step 3: Build filters and time range
+        # ------------------------------------------------------------------
+        warnings: list[str] = []
+        query_filters: list[dict[str, Any]] = [
+            {"col": f.col, "op": f.op, "val": f.val} for f in request.filters
+        ]
+        # Track all applied filters (including synthesized ones) for the response.
+        effective_filters: list[QueryDatasetFilter] = list(request.filters)
+        granularity: str | None = None
+
+        if request.time_range:
+            temporal_col = request.time_column or getattr(
+                dataset, "main_dttm_col", None
+            )
+            if not temporal_col:
+                await ctx.error("time_range provided but no temporal column available")
+                return DatasetError.create(
+                    error=(
+                        "time_range was provided but no temporal column is available. "
+                        "Either set time_column explicitly or ensure the dataset has "
+                        "a main datetime column configured."
+                    ),
+                    error_type="ValidationError",
+                )
+            # Validate that the temporal column actually exists on the dataset
+            if temporal_col not in valid_columns:
+                await ctx.error("time_column '%s' not found on dataset" % temporal_col)
+                return DatasetError.create(
+                    error=(
+                        f"time_column '{temporal_col}' does not exist on this dataset."
+                    ),
+                    error_type="ValidationError",
+                )
+            # Warn if the chosen temporal column isn't marked as datetime
+            dttm_cols = {c.column_name for c in dataset.columns if c.is_dttm}
+            if temporal_col not in dttm_cols:
+                warnings.append(
+                    f"Column '{temporal_col}' is not marked as a datetime "
+                    f"column on this dataset. Time filtering may not work "
+                    f"as expected."
+                )
+
+            query_filters.append(
+                {
+                    "col": temporal_col,
+                    "op": "TEMPORAL_RANGE",
+                    "val": request.time_range,
+                }
+            )
+            effective_filters.append(
+                QueryDatasetFilter(
+                    col=temporal_col,
+                    op="TEMPORAL_RANGE",
+                    val=request.time_range,
+                )
+            )
+            granularity = temporal_col
+            await ctx.debug(
+                "Time filter: column=%s, range=%s" % (temporal_col, request.time_range)
+            )
+
+        # ------------------------------------------------------------------
+        # Step 4: Build query dict
+        # ------------------------------------------------------------------
+        await ctx.report_progress(3, 5, "Building query")
+        query_dict: dict[str, Any] = {
+            "filters": query_filters,
+            "columns": request.columns,
+            "metrics": request.metrics,
+            "row_limit": request.row_limit,
+            "order_desc": request.order_desc,
+        }
+        if granularity:
+            query_dict["granularity"] = granularity
+        if request.order_by:
+            # OrderBy = tuple[Metric | Column, bool] where bool is ascending
+            query_dict["orderby"] = [
+                (col, not request.order_desc) for col in request.order_by
+            ]
+
+        await ctx.debug("Query dict keys: %s" % (sorted(query_dict.keys()),))
+
+        # ------------------------------------------------------------------
+        # Step 5: Create QueryContext and execute
+        # ------------------------------------------------------------------
+        await ctx.report_progress(4, 5, "Executing query")
+        start_time = time.time()
+
+        with event_logger.log_context(action="mcp.query_dataset.execute"):
+            factory = QueryContextFactory()
+            # datasource_type is "table" because this tool queries SqlaTable
+            # datasets (Superset's built-in semantic layer). External semantic
+            # layers (dbt, Snowflake Cortex, etc.) use "semantic_view" and have
+            # a different query path — see SemanticView + mapper.py.
+            query_context = factory.create(
+                datasource={"id": dataset.id, "type": "table"},
+                queries=[query_dict],
+                form_data={},
+                force=not request.use_cache or request.force_refresh,
+                custom_cache_timeout=request.cache_timeout,
+            )
+
+            command = ChartDataCommand(query_context)
+            command.validate()
+            result = command.run()
+
+        query_duration_ms = int((time.time() - start_time) * 1000)
+
+        if not result or "queries" not in result or len(result["queries"]) == 0:
+            await ctx.warning("Query returned no results for dataset %s" % dataset.id)
+            return DatasetError.create(
+                error="Query returned no results.",
+                error_type="EmptyQuery",
+            )
+
+        # ------------------------------------------------------------------
+        # Step 6: Format response
+        # ------------------------------------------------------------------
+        await ctx.report_progress(5, 5, "Formatting results")
+        query_result = result["queries"][0]
+        data = query_result.get("data", [])
+        raw_columns = query_result.get("colnames", [])
+
+        if not data:
+            return QueryDatasetResponse(
+                dataset_id=dataset.id,
+                dataset_name=dataset_name,
+                columns=[],
+                data=[],
+                row_count=0,
+                total_rows=0,
+                summary=f"Query on '{dataset_name}' returned no data.",
+                performance=PerformanceMetadata(
+                    query_duration_ms=query_duration_ms,
+                    cache_status="no_data",
+                ),
+                cache_status=get_cache_status_from_result(
+                    query_result, force_refresh=request.force_refresh
+                ),
+                applied_filters=effective_filters,
+                warnings=warnings,
+            )
+
+        # Build column metadata in a single pass per column.
+        # Cap stats computation at STATS_SAMPLE rows to avoid O(rows*cols)
+        # overhead on large result sets (row_limit allows up to 50k).
+        stats_sample_size = 5000
+        stats_rows = data[:stats_sample_size]
+
+        columns_meta: list[DataColumn] = []
+        for col_name in raw_columns:
+            sample_values = [
+                row.get(col_name) for row in data[:3] if row.get(col_name) is not None
+            ]
+            data_type = "string"
+            if sample_values:
+                if all(isinstance(v, bool) for v in sample_values):
+                    data_type = "boolean"
+                elif all(isinstance(v, (int, float)) for v in sample_values):
+                    data_type = "numeric"
+
+            # Compute null_count and unique non-null values in one pass
+            null_count = 0
+            unique_vals: set[str] = set()
+            for row in stats_rows:
+                val = row.get(col_name)
+                if val is None:
+                    null_count += 1
+                else:
+                    unique_vals.add(str(val))
+
+            columns_meta.append(
+                DataColumn(
+                    name=col_name,
+                    display_name=col_name.replace("_", " ").title(),
+                    data_type=data_type,
+                    sample_values=sample_values[:3],
+                    null_count=null_count,
+                    unique_count=len(unique_vals),
+                )
+            )
+
+        cache_status = get_cache_status_from_result(
+            query_result, force_refresh=request.force_refresh
+        )
+
+        cache_label = "cached" if cache_status and cache_status.cache_hit else "fresh"
+        summary = (
+            f"Dataset '{dataset_name}': {len(data)} rows, "
+            f"{len(raw_columns)} columns ({cache_label})."
+        )
+
+        await ctx.info(
+            "Query complete: rows=%s, columns=%s, duration=%sms"
+            % (len(data), len(raw_columns), query_duration_ms)
+        )
+
+        return QueryDatasetResponse(
+            dataset_id=dataset.id,
+            dataset_name=dataset_name,
+            columns=columns_meta,
+            data=data,
+            row_count=len(data),
+            total_rows=query_result.get("rowcount"),
+            summary=summary,
+            performance=PerformanceMetadata(
+                query_duration_ms=query_duration_ms,
+                cache_status=cache_label,
+            ),
+            cache_status=cache_status,
+            applied_filters=effective_filters,
+            warnings=warnings,
+        )
+
+    except OAuth2RedirectError as exc:
+        redirect_msg = build_oauth2_redirect_message(exc)
+        await ctx.error("OAuth2 redirect required: %s" % (redirect_msg,))
+        return DatasetError.create(
+            error=redirect_msg,
+            error_type="OAuth2Redirect",
+        )
+
+    except OAuth2Error as exc:
+        await ctx.error("OAuth2 error: %s" % (str(exc),))
+        return DatasetError.create(
+            error=f"OAuth2 authentication error: {exc}",
+            error_type="OAuth2Error",
+        )
+
+    except (CommandException, SupersetException) as exc:
+        await ctx.error("Query failed: %s" % (str(exc),))
+        return DatasetError.create(
+            error=f"Query execution failed: {exc}",
+            error_type="QueryError",
+        )
+
+    except SQLAlchemyError as exc:
+        await ctx.error("Database error: %s" % (str(exc),))
+        return DatasetError.create(
+            error=f"Database error: {exc}",
+            error_type="DatabaseError",
+        )
+
+    except Exception as exc:
+        logger.exception(
+            "Unexpected error while querying dataset: %s: %s",
+            type(exc).__name__,
+            str(exc),
+        )
+        await ctx.error("Unexpected error: %s: %s" % (type(exc).__name__, str(exc)))
+        return DatasetError.create(
+            error="An unexpected error occurred while querying the dataset.",
+            error_type="UnexpectedError",
+        )

superset/models/helpers.py

@@ -1585,11 +1585,17 @@ class ExploreMixin:  # pylint: disable=too-many-public-methods
                 for metric in metric_names
             }
 
-            # When the original query has limit or offset we wont apply those
-            # to the subquery so we prevent data inconsistency due to missing records
-            # in the dataframes when performing the join
+            # The subquery drops row_offset (the offset period's own row ordering
+            # differs from the main query's, so applying the same offset would skew
+            # the join). It must still fetch enough rows to cover the main query's
+            # window, hence row_limit + row_offset when a chart limit is set.
             if query_object.row_limit or query_object.row_offset:
-                query_object_clone_dct["row_limit"] = app.config["ROW_LIMIT"]
+                if query_object.row_limit:
+                    query_object_clone_dct["row_limit"] = (
+                        query_object.row_limit + query_object.row_offset
+                    )
+                else:
+                    query_object_clone_dct["row_limit"] = app.config["ROW_LIMIT"]
                 query_object_clone_dct["row_offset"] = 0
 
             # Call the unified query method on the datasource

tests/integration_tests/query_context_tests.py

@@ -23,7 +23,6 @@ from unittest.mock import Mock, patch
 import numpy as np
 import pandas as pd
 import pytest
-from flask import current_app
 from pandas import DateOffset
 
 from superset import db
@@ -43,6 +42,7 @@ from superset.utils.core import (
     QueryStatus,
 )
 from superset.utils.pandas_postprocessing.utils import FLAT_COLUMN_SEPARATOR
+from tests.conftest import with_config
 from tests.integration_tests.base_tests import SupersetTestCase
 from tests.integration_tests.conftest import (
     only_postgresql,
@@ -68,6 +68,130 @@ def get_sql_text(payload: dict[str, Any]) -> str:
     return response["query"]
 
 
+def _time_comparison_offset_queries_payload() -> dict[str, Any]:
+    """Birth-names chart payload with time comparison and x-axis suitable for tests."""
+    payload = get_query_context("birth_names")
+    payload["queries"][0]["columns"] = [
+        {
+            "timeGrain": "P1D",
+            "columnType": "BASE_AXIS",
+            "sqlExpression": "ds",
+            "label": "ds",
+            "expressionType": "SQL",
+        }
+    ]
+    payload["queries"][0]["metrics"] = ["sum__num"]
+    payload["queries"][0]["groupby"] = ["name"]
+    payload["queries"][0]["is_timeseries"] = True
+    payload["queries"][0]["time_range"] = "1990 : 1991"
+    return payload
+
+
+@pytest.mark.usefixtures("load_birth_names_dashboard_with_slices")
+@patch("superset.common.query_context.QueryContext.get_query_result")
+def test_time_offset_comparison_queries_use_chart_row_limit(
+    query_result_mock: Mock,
+) -> None:
+    """Comparison SQL covers the main query's window (row_limit + row_offset)."""
+    payload = _time_comparison_offset_queries_payload()
+    payload["queries"][0]["row_limit"] = 100
+    payload["queries"][0]["row_offset"] = 10
+
+    initial_df = pd.DataFrame(
+        {
+            "__timestamp": ["1990-01-01", "1990-01-01"],
+            "name": ["zban", "ahwb"],
+            "sum__num": [43571, 27225],
+        }
+    )
+    mock_query_result = Mock()
+    mock_query_result.df = initial_df
+    query_result_mock.side_effect = [mock_query_result]
+
+    query_context = ChartDataQueryContextSchema().load(payload)
+    query_object = query_context.queries[0]
+    df = query_context.get_query_result(query_object).df
+
+    payload["queries"][0]["time_offsets"] = ["1 year ago", "1 year later"]
+    query_context = ChartDataQueryContextSchema().load(payload)
+    query_object = query_context.queries[0]
+
+    def cache_key_fn(qo: QueryObject, time_offset: str, time_grain: Any) -> str | None:
+        return query_context._processor.query_cache_key(
+            qo, time_offset=time_offset, time_grain=time_grain
+        )
+
+    def cache_timeout_fn() -> int:
+        return query_context._processor.get_cache_timeout()
+
+    time_offsets_obj = query_context.datasource.processing_time_offsets(
+        df, query_object, cache_key_fn, cache_timeout_fn, query_context.force
+    )
+    sqls = time_offsets_obj["queries"]
+    assert len(sqls) == 2
+    assert re.search(r"1989-01-01.+1990-01-01", sqls[0], re.S)
+    assert re.search(r"LIMIT 110", sqls[0], re.S)
+    assert not re.search(r"OFFSET 10", sqls[0], re.S)
+    assert re.search(r"1991-01-01.+1992-01-01", sqls[1], re.S)
+    assert re.search(r"LIMIT 110", sqls[1], re.S)
+    assert not re.search(r"OFFSET 10", sqls[1], re.S)
+
+
+@pytest.mark.usefixtures("load_birth_names_dashboard_with_slices")
+@with_config({"ROW_LIMIT": 4242})
+@patch("superset.common.query_context.QueryContext.get_query_result")
+def test_time_offset_comparison_queries_use_config_row_limit_without_chart_limit(
+    query_result_mock: Mock,
+) -> None:
+    """Chart with row_offset only: subquery widens the config ROW_LIMIT by row_offset.
+
+    The schema fills `row_limit` with `app.config["ROW_LIMIT"]` when the payload
+    omits it, so the query_object arrives with row_limit=4242. The subquery then
+    covers the window via row_limit + row_offset = 4252.
+    """
+    payload = _time_comparison_offset_queries_payload()
+    del payload["queries"][0]["row_limit"]
+    payload["queries"][0]["row_offset"] = 10
+
+    initial_df = pd.DataFrame(
+        {
+            "__timestamp": ["1990-01-01", "1990-01-01"],
+            "name": ["zban", "ahwb"],
+            "sum__num": [43571, 27225],
+        }
+    )
+    mock_query_result = Mock()
+    mock_query_result.df = initial_df
+    query_result_mock.side_effect = [mock_query_result]
+
+    query_context = ChartDataQueryContextSchema().load(payload)
+    query_object = query_context.queries[0]
+    df = query_context.get_query_result(query_object).df
+
+    payload["queries"][0]["time_offsets"] = ["1 year ago", "1 year later"]
+    query_context = ChartDataQueryContextSchema().load(payload)
+    query_object = query_context.queries[0]
+
+    def cache_key_fn(qo: QueryObject, time_offset: str, time_grain: Any) -> str | None:
+        return query_context._processor.query_cache_key(
+            qo, time_offset=time_offset, time_grain=time_grain
+        )
+
+    def cache_timeout_fn() -> int:
+        return query_context._processor.get_cache_timeout()
+
+    time_offsets_obj = query_context.datasource.processing_time_offsets(
+        df, query_object, cache_key_fn, cache_timeout_fn, query_context.force
+    )
+    sqls = time_offsets_obj["queries"]
+    limit_pattern = re.compile(r"LIMIT\s+4252\b")
+    assert len(sqls) == 2
+    assert limit_pattern.search(sqls[0])
+    assert not re.search(r"OFFSET 10", sqls[0], re.S)
+    assert limit_pattern.search(sqls[1])
+    assert not re.search(r"OFFSET 10", sqls[1], re.S)
+
+
 @pytest.mark.skip(
     reason=(
         "TODO: Fix test class to work with DuckDB example data format. "
@@ -794,28 +918,17 @@ class TestQueryContext(SupersetTestCase):
 
     @pytest.mark.usefixtures("load_birth_names_dashboard_with_slices")
     @patch("superset.common.query_context.QueryContext.get_query_result")
-    def test_time_offsets_in_query_object_no_limit(self, query_result_mock):
+    def test_time_offsets_in_query_object_uses_chart_row_limit(self, query_result_mock):
         """
-        Ensure that time_offsets can generate the correct queries and
-        it doesnt use the row_limit nor row_offset from the original
-        query object
+        Subquery honors the chart's row_limit (widened by row_offset so the
+        LEFT JOIN covers the main query's paginated window) and drops
+        row_offset. Before this fix, row_limit was replaced with
+        app.config["ROW_LIMIT"], which caused the main query and offset
+        subquery to fetch different row counts.
         """
-        payload = get_query_context("birth_names")
-        payload["queries"][0]["columns"] = [
-            {
-                "timeGrain": "P1D",
-                "columnType": "BASE_AXIS",
-                "sqlExpression": "ds",
-                "label": "ds",
-                "expressionType": "SQL",
-            }
-        ]
-        payload["queries"][0]["metrics"] = ["sum__num"]
-        payload["queries"][0]["groupby"] = ["name"]
-        payload["queries"][0]["is_timeseries"] = True
+        payload = _time_comparison_offset_queries_payload()
         payload["queries"][0]["row_limit"] = 100
         payload["queries"][0]["row_offset"] = 10
-        payload["queries"][0]["time_range"] = "1990 : 1991"
 
         initial_data = {
             "__timestamp": ["1990-01-01", "1990-01-01"],
@@ -839,33 +952,86 @@ class TestQueryContext(SupersetTestCase):
         query_context = ChartDataQueryContextSchema().load(payload)
         query_object = query_context.queries[0]
 
-        def cache_key_fn(qo, time_offset, time_grain):
+        def cache_key_fn(
+            qo: QueryObject, time_offset: str, time_grain: Any
+        ) -> str | None:
             return query_context._processor.query_cache_key(
                 qo, time_offset=time_offset, time_grain=time_grain
             )
 
-        def cache_timeout_fn():
+        def cache_timeout_fn() -> int:
             return query_context._processor.get_cache_timeout()
 
         time_offsets_obj = query_context.datasource.processing_time_offsets(
             df, query_object, cache_key_fn, cache_timeout_fn, query_context.force
         )
         sqls = time_offsets_obj["queries"]
-        row_limit_value = current_app.config["ROW_LIMIT"]
-        row_limit_pattern_with_config_value = r"LIMIT " + re.escape(
-            str(row_limit_value)
-        )
         assert len(sqls) == 2
-        # 1 year ago
+        # 1 year ago — subquery widens row_limit to cover main window (100 + 10)
         assert re.search(r"1989-01-01.+1990-01-01", sqls[0], re.S)
-        assert not re.search(r"LIMIT 100", sqls[0], re.S)
+        assert re.search(r"LIMIT 110", sqls[0], re.S)
         assert not re.search(r"OFFSET 10", sqls[0], re.S)
-        assert re.search(row_limit_pattern_with_config_value, sqls[0], re.S)
         # 1 year later
         assert re.search(r"1991-01-01.+1992-01-01", sqls[1], re.S)
-        assert not re.search(r"LIMIT 100", sqls[1], re.S)
+        assert re.search(r"LIMIT 110", sqls[1], re.S)
+        assert not re.search(r"OFFSET 10", sqls[1], re.S)
+
+    @pytest.mark.usefixtures("load_birth_names_dashboard_with_slices")
+    @with_config({"ROW_LIMIT": 4242})
+    @patch("superset.common.query_context.QueryContext.get_query_result")
+    def test_time_offsets_use_config_row_limit_when_chart_has_offset_only(
+        self, query_result_mock
+    ):
+        """
+        Chart with row_offset only: subquery widens the config ROW_LIMIT by row_offset.
+
+        The schema fills row_limit with app.config["ROW_LIMIT"] (4242) when the
+        payload omits it, so the subquery covers the window via 4242 + 10 = 4252.
+        """
+        payload = _time_comparison_offset_queries_payload()
+        del payload["queries"][0]["row_limit"]
+        payload["queries"][0]["row_offset"] = 10
+
+        initial_data = {
+            "__timestamp": ["1990-01-01", "1990-01-01"],
+            "name": ["zban", "ahwb"],
+            "sum__num": [43571, 27225],
+        }
+        initial_df = pd.DataFrame(initial_data)
+
+        mock_query_result = Mock()
+        mock_query_result.df = initial_df
+        query_result_mock.side_effect = [mock_query_result]
+
+        query_context = ChartDataQueryContextSchema().load(payload)
+        query_object = query_context.queries[0]
+        query_result = query_context.get_query_result(query_object)
+        df = query_result.df
+
+        payload["queries"][0]["time_offsets"] = ["1 year ago", "1 year later"]
+        query_context = ChartDataQueryContextSchema().load(payload)
+        query_object = query_context.queries[0]
+
+        def cache_key_fn(
+            qo: QueryObject, time_offset: str, time_grain: Any
+        ) -> str | None:
+            return query_context._processor.query_cache_key(
+                qo, time_offset=time_offset, time_grain=time_grain
+            )
+
+        def cache_timeout_fn() -> int:
+            return query_context._processor.get_cache_timeout()
+
+        time_offsets_obj = query_context.datasource.processing_time_offsets(
+            df, query_object, cache_key_fn, cache_timeout_fn, query_context.force
+        )
+        sqls = time_offsets_obj["queries"]
+        limit_pattern = re.compile(r"LIMIT\s+4252\b")
+        assert len(sqls) == 2
+        assert limit_pattern.search(sqls[0])
+        assert not re.search(r"OFFSET 10", sqls[0], re.S)
+        assert limit_pattern.search(sqls[1])
         assert not re.search(r"OFFSET 10", sqls[1], re.S)
-        assert re.search(row_limit_pattern_with_config_value, sqls[1], re.S)
 
 
 def test_get_label_map(app_context, virtual_dataset_comma_in_column_value):

tests/unit_tests/common/test_query_context_processor.py

@@ -700,6 +700,233 @@ def test_processing_time_offsets_date_range_enabled(processor):
                             assert isinstance(result["cache_keys"], list)
 
 
+def test_processing_time_offsets_uses_chart_row_limit(processor):
+    """Offset subquery inherits the chart's row_limit when one is set."""
+    from superset.common.query_object import QueryObject
+    from superset.models.helpers import ExploreMixin
+
+    processor._qc_datasource.processing_time_offsets = (
+        ExploreMixin.processing_time_offsets.__get__(processor._qc_datasource)
+    )
+
+    df = pd.DataFrame({"__timestamp": ["1990-01-01"], "sum__num": [100]})
+
+    query_object = QueryObject(
+        datasource=MagicMock(),
+        granularity="ds",
+        columns=[],
+        metrics=["sum__num"],
+        is_timeseries=True,
+        row_limit=100,
+        row_offset=0,
+        time_offsets=["1 year ago"],
+        filters=[
+            {
+                "col": "ds",
+                "op": "TEMPORAL_RANGE",
+                "val": "1990-01-01 : 1991-01-01",
+            }
+        ],
+    )
+
+    captured: list[dict[str, Any]] = []
+
+    def fake_query(dct: dict[str, Any]) -> MagicMock:
+        captured.append(dct)
+        result = MagicMock()
+        result.df = pd.DataFrame()
+        result.query = "SELECT 1"
+        return result
+
+    processor._qc_datasource.query = fake_query
+    processor._qc_datasource.normalize_df = MagicMock(return_value=pd.DataFrame())
+
+    with (
+        patch(
+            "superset.models.helpers.get_since_until_from_query_object",
+            return_value=(pd.Timestamp("1990-01-01"), pd.Timestamp("1991-01-01")),
+        ),
+        patch(
+            "superset.common.utils.query_cache_manager.QueryCacheManager"
+        ) as mock_cache_manager,
+        patch.object(
+            processor._qc_datasource,
+            "get_time_grain",
+            return_value=None,
+        ),
+        patch.object(
+            processor._qc_datasource,
+            "join_offset_dfs",
+            return_value=df,
+        ),
+    ):
+        mock_cache = MagicMock()
+        mock_cache.is_loaded = False
+        mock_cache_manager.get.return_value = mock_cache
+
+        processor._qc_datasource.processing_time_offsets(
+            df, query_object, None, None, False
+        )
+
+    assert len(captured) == 1
+    assert captured[0]["row_limit"] == 100
+    assert captured[0]["row_offset"] == 0
+
+
+def test_processing_time_offsets_row_offset_extends_window(processor):
+    """Offset subquery limit covers the main query's window (row_limit + row_offset).
+
+    When the chart has pagination (row_offset > 0), fetching only row_limit rows
+    in the offset period would likely miss the dimensions present in the main
+    query's page, yielding null comparison values. The subquery instead drops
+    row_offset and widens row_limit to cover the full window.
+    """
+    from superset.common.query_object import QueryObject
+    from superset.models.helpers import ExploreMixin
+
+    processor._qc_datasource.processing_time_offsets = (
+        ExploreMixin.processing_time_offsets.__get__(processor._qc_datasource)
+    )
+
+    df = pd.DataFrame({"__timestamp": ["1990-01-01"], "sum__num": [100]})
+
+    query_object = QueryObject(
+        datasource=MagicMock(),
+        granularity="ds",
+        columns=[],
+        metrics=["sum__num"],
+        is_timeseries=True,
+        row_limit=100,
+        row_offset=10,
+        time_offsets=["1 year ago"],
+        filters=[
+            {
+                "col": "ds",
+                "op": "TEMPORAL_RANGE",
+                "val": "1990-01-01 : 1991-01-01",
+            }
+        ],
+    )
+
+    captured: list[dict[str, Any]] = []
+
+    def fake_query(dct: dict[str, Any]) -> MagicMock:
+        captured.append(dct)
+        result = MagicMock()
+        result.df = pd.DataFrame()
+        result.query = "SELECT 1"
+        return result
+
+    processor._qc_datasource.query = fake_query
+    processor._qc_datasource.normalize_df = MagicMock(return_value=pd.DataFrame())
+
+    with (
+        patch(
+            "superset.models.helpers.get_since_until_from_query_object",
+            return_value=(pd.Timestamp("1990-01-01"), pd.Timestamp("1991-01-01")),
+        ),
+        patch(
+            "superset.common.utils.query_cache_manager.QueryCacheManager"
+        ) as mock_cache_manager,
+        patch.object(
+            processor._qc_datasource,
+            "get_time_grain",
+            return_value=None,
+        ),
+        patch.object(
+            processor._qc_datasource,
+            "join_offset_dfs",
+            return_value=df,
+        ),
+    ):
+        mock_cache = MagicMock()
+        mock_cache.is_loaded = False
+        mock_cache_manager.get.return_value = mock_cache
+
+        processor._qc_datasource.processing_time_offsets(
+            df, query_object, None, None, False
+        )
+
+    assert len(captured) == 1
+    assert captured[0]["row_limit"] == 110
+    assert captured[0]["row_offset"] == 0
+
+
+def test_processing_time_offsets_falls_back_to_config_row_limit(processor):
+    """Offset subquery uses app config ROW_LIMIT when chart has offset but no limit."""
+    from superset.common.query_object import QueryObject
+    from superset.models.helpers import ExploreMixin
+
+    processor._qc_datasource.processing_time_offsets = (
+        ExploreMixin.processing_time_offsets.__get__(processor._qc_datasource)
+    )
+
+    df = pd.DataFrame({"__timestamp": ["1990-01-01"], "sum__num": [100]})
+
+    query_object = QueryObject(
+        datasource=MagicMock(),
+        granularity="ds",
+        columns=[],
+        metrics=["sum__num"],
+        is_timeseries=True,
+        row_limit=None,
+        row_offset=10,
+        time_offsets=["1 year ago"],
+        filters=[
+            {
+                "col": "ds",
+                "op": "TEMPORAL_RANGE",
+                "val": "1990-01-01 : 1991-01-01",
+            }
+        ],
+    )
+
+    captured: list[dict[str, Any]] = []
+
+    def fake_query(dct: dict[str, Any]) -> MagicMock:
+        captured.append(dct)
+        result = MagicMock()
+        result.df = pd.DataFrame()
+        result.query = "SELECT 1"
+        return result
+
+    processor._qc_datasource.query = fake_query
+    processor._qc_datasource.normalize_df = MagicMock(return_value=pd.DataFrame())
+
+    with (
+        patch(
+            "superset.models.helpers.get_since_until_from_query_object",
+            return_value=(pd.Timestamp("1990-01-01"), pd.Timestamp("1991-01-01")),
+        ),
+        patch(
+            "superset.common.utils.query_cache_manager.QueryCacheManager"
+        ) as mock_cache_manager,
+        patch.object(
+            processor._qc_datasource,
+            "get_time_grain",
+            return_value=None,
+        ),
+        patch.object(
+            processor._qc_datasource,
+            "join_offset_dfs",
+            return_value=df,
+        ),
+        patch("superset.models.helpers.app") as mock_app,
+    ):
+        mock_app.config = {"ROW_LIMIT": 4242}
+        mock_cache = MagicMock()
+        mock_cache.is_loaded = False
+        mock_cache_manager.get.return_value = mock_cache
+
+        processor._qc_datasource.processing_time_offsets(
+            df, query_object, None, None, False
+        )
+
+    assert len(captured) == 1
+    assert captured[0]["row_limit"] == 4242
+    assert captured[0]["row_offset"] == 0
+
+
 def test_ensure_totals_available_updates_cache_values():
     """
     Test that ensure_totals_available() updates the query objects AND

tests/unit_tests/db_engine_specs/test_gsheets.py

@@ -24,6 +24,7 @@ import pandas as pd
 import pytest
 from pytest_mock import MockerFixture
 from requests.exceptions import HTTPError
+from shillelagh.exceptions import UnauthenticatedError
 from sqlalchemy.engine.url import make_url
 
 from superset.errors import ErrorLevel, SupersetError, SupersetErrorType
@@ -789,6 +790,36 @@ def test_needs_oauth2_with_other_error(mocker: MockerFixture) -> None:
     assert GSheetsEngineSpec.needs_oauth2(ex) is False
 
 
+def test_needs_oauth2_with_shillelagh_unauthenticated_error(
+    mocker: MockerFixture,
+) -> None:
+    """
+    Test that needs_oauth2 returns True when UnauthenticatedError is raised.
+    """
+    from superset.db_engine_specs.gsheets import GSheetsEngineSpec
+
+    g = mocker.patch("superset.db_engine_specs.gsheets.g")
+    g.user = mocker.MagicMock()
+
+    ex = UnauthenticatedError("Token has been revoked")
+    assert GSheetsEngineSpec.needs_oauth2(ex) is True
+
+
+def test_needs_oauth2_with_unrelated_exception_type(
+    mocker: MockerFixture,
+) -> None:
+    """
+    Test that an unrelated exception type (with no matching message) returns
+    False.
+    """
+    from superset.db_engine_specs.gsheets import GSheetsEngineSpec
+
+    g = mocker.patch("superset.db_engine_specs.gsheets.g")
+    g.user = mocker.MagicMock()
+
+    assert GSheetsEngineSpec.needs_oauth2(ValueError("unrelated")) is False
+
+
 def test_get_oauth2_fresh_token_success(
     mocker: MockerFixture,
     oauth2_config: OAuth2ClientConfig,

tests/unit_tests/mcp_service/chart/tool/test_list_charts.py

@@ -320,66 +320,13 @@ class TestChartDataModelMetadataPrivacy:
         assert data["error_type"] == DATA_MODEL_METADATA_ERROR_TYPE
 
 
-class TestListChartsCreatedByMe:
-    """Tests for the created_by_me flag on ListChartsRequest."""
-
-    def test_created_by_me_default_is_false(self):
-        request = ListChartsRequest()
-        assert request.created_by_me is False
-
-    def test_created_by_me_true_accepted(self):
-        request = ListChartsRequest(created_by_me=True)
-        assert request.created_by_me is True
-
-    def test_created_by_me_combined_with_filters(self):
-        request = ListChartsRequest(
-            created_by_me=True,
-            filters=[ChartFilter(col="slice_name", opr="sw", value="My")],
-        )
-        assert request.created_by_me is True
-        assert len(request.filters) == 1
-
-    def test_created_by_me_with_search_raises(self):
-        from pydantic import ValidationError
-
-        with pytest.raises(ValidationError, match="created_by_me"):
-            ListChartsRequest(created_by_me=True, search="My charts")
-
-    def test_chart_filter_rejects_created_by_fk(self):
-        """created_by_fk is not a public filter column; use created_by_me instead."""
-        from pydantic import ValidationError
-
-        with pytest.raises(ValidationError):
-            ChartFilter(col="created_by_fk", opr="eq", value=1)
-
-
-class TestListChartsOwnedByMe:
-    """Tests for the owned_by_me flag on ListChartsRequest."""
-
-    def test_owned_by_me_default_is_false(self):
-        request = ListChartsRequest()
-        assert request.owned_by_me is False
-
-    def test_owned_by_me_true_accepted(self):
-        request = ListChartsRequest(owned_by_me=True)
-        assert request.owned_by_me is True
-
-    def test_owned_by_me_combined_with_filters(self):
-        request = ListChartsRequest(
-            owned_by_me=True,
-            filters=[ChartFilter(col="slice_name", opr="sw", value="My")],
-        )
-        assert request.owned_by_me is True
-        assert len(request.filters) == 1
-
-    def test_owned_by_me_with_search_raises(self):
-        from pydantic import ValidationError
-
-        with pytest.raises(ValidationError, match="owned_by_me"):
-            ListChartsRequest(owned_by_me=True, search="My charts")
-
-    def test_owned_by_me_and_created_by_me_allowed(self):
-        """Both flags together are valid (OR logic — creator or owner)."""
-        request = ListChartsRequest(owned_by_me=True, created_by_me=True)
-        assert request.owned_by_me is True
-        assert request.created_by_me is True
+@patch("superset.daos.chart.ChartDAO.list")
+@pytest.mark.asyncio
+async def test_list_charts_no_arguments(mock_list, mcp_server):
+    """Regression test: list_charts must accept zero arguments without raising
+    pydantic_core.ValidationError: Missing required argument: request."""
+    mock_list.return_value = ([], 0)
+    async with Client(mcp_server) as client:
+        result = await client.call_tool("list_charts", {})
+    data = json.loads(result.content[0].text)
+    assert "charts" in data

tests/unit_tests/mcp_service/dashboard/tool/test_dashboard_tools.py

@@ -30,7 +30,6 @@ from flask import g
 
 from superset.mcp_service.app import mcp
 from superset.mcp_service.dashboard.schemas import (
-    DashboardFilter,
     ListDashboardsRequest,
 )
 from superset.mcp_service.dashboard.tool.get_dashboard_info import (
@@ -1355,66 +1354,13 @@ class TestDashboardSortableColumns:
             assert col in list_dashboards.__doc__
 
 
-class TestListDashboardsCreatedByMe:
-    """Tests for the created_by_me flag on ListDashboardsRequest."""
-
-    def test_created_by_me_default_is_false(self):
-        request = ListDashboardsRequest()
-        assert request.created_by_me is False
-
-    def test_created_by_me_true_accepted(self):
-        request = ListDashboardsRequest(created_by_me=True)
-        assert request.created_by_me is True
-
-    def test_created_by_me_combined_with_filters(self):
-        request = ListDashboardsRequest(
-            created_by_me=True,
-            filters=[DashboardFilter(col="published", opr="eq", value=True)],
-        )
-        assert request.created_by_me is True
-        assert len(request.filters) == 1
-
-    def test_created_by_me_with_search_raises(self):
-        from pydantic import ValidationError
-
-        with pytest.raises(ValidationError, match="created_by_me"):
-            ListDashboardsRequest(created_by_me=True, search="My dashboards")
-
-    def test_dashboard_filter_rejects_created_by_fk(self):
-        """created_by_fk is not a public filter column; use created_by_me instead."""
-        from pydantic import ValidationError
-
-        with pytest.raises(ValidationError):
-            DashboardFilter(col="created_by_fk", opr="eq", value=1)
-
-
-class TestListDashboardsOwnedByMe:
-    """Tests for the owned_by_me flag on ListDashboardsRequest."""
-
-    def test_owned_by_me_default_is_false(self):
-        request = ListDashboardsRequest()
-        assert request.owned_by_me is False
-
-    def test_owned_by_me_true_accepted(self):
-        request = ListDashboardsRequest(owned_by_me=True)
-        assert request.owned_by_me is True
-
-    def test_owned_by_me_combined_with_filters(self):
-        request = ListDashboardsRequest(
-            owned_by_me=True,
-            filters=[DashboardFilter(col="published", opr="eq", value=True)],
-        )
-        assert request.owned_by_me is True
-        assert len(request.filters) == 1
-
-    def test_owned_by_me_with_search_raises(self):
-        from pydantic import ValidationError
-
-        with pytest.raises(ValidationError, match="owned_by_me"):
-            ListDashboardsRequest(owned_by_me=True, search="My dashboards")
-
-    def test_owned_by_me_and_created_by_me_allowed(self):
-        """Both flags together are valid (OR logic — creator or owner)."""
-        request = ListDashboardsRequest(owned_by_me=True, created_by_me=True)
-        assert request.owned_by_me is True
-        assert request.created_by_me is True
+@patch("superset.daos.dashboard.DashboardDAO.list")
+@pytest.mark.asyncio
+async def test_list_dashboards_no_arguments(mock_list, mcp_server):
+    """Regression test: list_dashboards must accept zero arguments without raising
+    pydantic_core.ValidationError: Missing required argument: request."""
+    mock_list.return_value = ([], 0)
+    async with Client(mcp_server) as client:
+        result = await client.call_tool("list_dashboards", {})
+    data = json.loads(result.content[0].text)
+    assert "dashboards" in data

tests/unit_tests/mcp_service/dataset/tool/test_query_dataset.py

@@ -0,0 +1,831 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Tests for the query_dataset MCP tool."""
+
+from __future__ import annotations
+
+import importlib
+from collections.abc import Generator
+from typing import Any
+from unittest.mock import MagicMock, Mock, patch
+
+import pytest
+from fastmcp import Client, FastMCP
+
+from superset.mcp_service.app import mcp
+from superset.utils import json
+
+query_dataset_module = importlib.import_module(
+    "superset.mcp_service.dataset.tool.query_dataset"
+)
+
+
+@pytest.fixture
+def mcp_server() -> FastMCP:
+    return mcp
+
+
+@pytest.fixture(autouse=True)
+def mock_auth() -> Generator[MagicMock, None, None]:
+    """Mock authentication and metadata access for all tests."""
+    with (
+        patch("superset.mcp_service.auth.get_user_from_request") as mock_get_user,
+        patch.object(
+            query_dataset_module,
+            "user_can_view_data_model_metadata",
+            return_value=True,
+        ),
+    ):
+        mock_user = Mock()
+        mock_user.id = 1
+        mock_user.username = "admin"
+        mock_get_user.return_value = mock_user
+        yield mock_get_user
+
+
+def _make_column(name: str, is_dttm: bool = False) -> MagicMock:
+    """Build a mock SqlaTable column with the given name and datetime flag."""
+    col = MagicMock()
+    col.column_name = name
+    col.is_dttm = is_dttm
+    col.verbose_name = None
+    col.type = "VARCHAR"
+    col.groupby = True
+    col.filterable = True
+    col.description = None
+    return col
+
+
+def _make_metric(name: str, expression: str = "COUNT(*)") -> MagicMock:
+    """Build a mock SqlMetric with the given name and SQL expression."""
+    metric = MagicMock()
+    metric.metric_name = name
+    metric.verbose_name = None
+    metric.expression = expression
+    metric.description = None
+    metric.d3format = None
+    return metric
+
+
+def _make_dataset(
+    dataset_id: int = 1,
+    table_name: str = "orders",
+    columns: list[Any] | None = None,
+    metrics: list[Any] | None = None,
+    main_dttm_col: str | None = None,
+) -> MagicMock:
+    """Build a mock SqlaTable dataset with default columns and metrics."""
+    ds = MagicMock()
+    ds.id = dataset_id
+    ds.table_name = table_name
+    ds.uuid = f"test-uuid-{dataset_id}"
+    ds.main_dttm_col = main_dttm_col
+    ds.database = MagicMock()
+    ds.database.database_name = "examples"
+    ds.columns = columns or [
+        _make_column("category"),
+        _make_column("region"),
+        _make_column("order_date", is_dttm=True),
+    ]
+    ds.metrics = metrics or [
+        _make_metric("count", "COUNT(*)"),
+        _make_metric("total_revenue", "SUM(revenue)"),
+    ]
+    return ds
+
+
+def _mock_command_result(
+    data: list[dict[str, Any]] | None = None,
+    colnames: list[str] | None = None,
+) -> dict[str, Any]:
+    """Build the result dict that ChartDataCommand.run() returns."""
+    data = data or [
+        {"category": "Electronics", "count": 42},
+        {"category": "Clothing", "count": 17},
+    ]
+    colnames = colnames or ["category", "count"]
+    return {
+        "queries": [
+            {
+                "data": data,
+                "colnames": colnames,
+                "rowcount": len(data),
+                "cache_key": "abc123",
+                "is_cached": False,
+                "cached_dttm": None,
+                "cache_timeout": 300,
+            }
+        ]
+    }
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_success(mcp_server: FastMCP) -> None:
+    """Happy path: metrics + columns returns data."""
+    dataset = _make_dataset()
+    result_data = _mock_command_result()
+
+    with (
+        patch.object(
+            query_dataset_module,
+            "_resolve_dataset",
+            return_value=dataset,
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.validate",
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.run",
+            return_value=result_data,
+        ),
+        patch(
+            "superset.common.query_context_factory.QueryContextFactory.create",
+            return_value=MagicMock(),
+        ),
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "metrics": ["count"],
+                        "columns": ["category"],
+                    }
+                },
+            )
+
+    data = json.loads(result.content[0].text)
+    assert data["dataset_id"] == 1
+    assert data["dataset_name"] == "orders"
+    assert data["row_count"] == 2
+    assert len(data["data"]) == 2
+    assert data["data"][0]["category"] == "Electronics"
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_not_found(mcp_server: FastMCP) -> None:
+    """Dataset ID that doesn't exist returns error."""
+    with patch.object(
+        query_dataset_module,
+        "_resolve_dataset",
+        return_value=None,
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 999,
+                        "metrics": ["count"],
+                    }
+                },
+            )
+
+    data = json.loads(result.content[0].text)
+    assert data["error_type"] == "NotFound"
+    assert "999" in data["error"]
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_invalid_metric(mcp_server: FastMCP) -> None:
+    """Unknown metric name returns validation error with suggestions."""
+    dataset = _make_dataset()
+
+    with patch.object(
+        query_dataset_module,
+        "_resolve_dataset",
+        return_value=dataset,
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "metrics": ["countt"],  # typo
+                    }
+                },
+            )
+
+    data = json.loads(result.content[0].text)
+    assert data["error_type"] == "ValidationError"
+    assert "countt" in data["error"]
+    # Should suggest "count" as a close match
+    assert "count" in data["error"]
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_invalid_column(mcp_server: FastMCP) -> None:
+    """Unknown column name returns validation error."""
+    dataset = _make_dataset()
+
+    with patch.object(
+        query_dataset_module,
+        "_resolve_dataset",
+        return_value=dataset,
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "columns": ["nonexistent_col"],
+                        "metrics": ["count"],
+                    }
+                },
+            )
+
+    data = json.loads(result.content[0].text)
+    assert data["error_type"] == "ValidationError"
+    assert "nonexistent_col" in data["error"]
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_no_metrics_no_columns(mcp_server: FastMCP) -> None:
+    """Providing neither metrics nor columns raises validation error."""
+    from fastmcp.exceptions import ToolError
+
+    async with Client(mcp_server) as client:
+        with pytest.raises(ToolError, match="metrics.*columns"):
+            await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "metrics": [],
+                        "columns": [],
+                    }
+                },
+            )
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_with_time_range(mcp_server: FastMCP) -> None:
+    """time_range is converted to TEMPORAL_RANGE filter + granularity."""
+    dataset = _make_dataset(main_dttm_col="order_date")
+    result_data = _mock_command_result()
+    captured_queries: list[dict[str, Any]] = []
+
+    def capture_create(**kwargs):
+        captured_queries.extend(kwargs.get("queries", []))
+        return MagicMock()
+
+    with (
+        patch.object(
+            query_dataset_module,
+            "_resolve_dataset",
+            return_value=dataset,
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.validate",
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.run",
+            return_value=result_data,
+        ),
+        patch(
+            "superset.common.query_context_factory.QueryContextFactory.create",
+            side_effect=capture_create,
+        ),
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "metrics": ["count"],
+                        "time_range": "Last 7 days",
+                    }
+                },
+            )
+
+    assert len(captured_queries) == 1
+    query_dict = captured_queries[0]
+    # Should have TEMPORAL_RANGE filter
+    temporal_filters = [f for f in query_dict["filters"] if f["op"] == "TEMPORAL_RANGE"]
+    assert len(temporal_filters) == 1
+    assert temporal_filters[0]["col"] == "order_date"
+    assert temporal_filters[0]["val"] == "Last 7 days"
+    # Should set granularity
+    assert query_dict["granularity"] == "order_date"
+    # applied_filters in response must include the synthesized TEMPORAL_RANGE filter
+    data = json.loads(result.content[0].text)
+    resp_filters = data["applied_filters"]
+    temporal_resp = [f for f in resp_filters if f["op"] == "TEMPORAL_RANGE"]
+    assert len(temporal_resp) == 1
+    assert temporal_resp[0]["col"] == "order_date"
+    assert temporal_resp[0]["val"] == "Last 7 days"
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_time_range_no_temporal_column(mcp_server: FastMCP) -> None:
+    """time_range without a temporal column returns error."""
+    dataset = _make_dataset(main_dttm_col=None)
+
+    with patch.object(
+        query_dataset_module,
+        "_resolve_dataset",
+        return_value=dataset,
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "metrics": ["count"],
+                        "time_range": "Last 7 days",
+                    }
+                },
+            )
+
+    data = json.loads(result.content[0].text)
+    assert data["error_type"] == "ValidationError"
+    assert "temporal column" in data["error"].lower()
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_with_filters(mcp_server: FastMCP) -> None:
+    """User-provided filters are passed through to the query."""
+    dataset = _make_dataset()
+    result_data = _mock_command_result()
+    captured_queries: list[dict[str, Any]] = []
+
+    def capture_create(**kwargs):
+        captured_queries.extend(kwargs.get("queries", []))
+        return MagicMock()
+
+    with (
+        patch.object(
+            query_dataset_module,
+            "_resolve_dataset",
+            return_value=dataset,
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.validate",
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.run",
+            return_value=result_data,
+        ),
+        patch(
+            "superset.common.query_context_factory.QueryContextFactory.create",
+            side_effect=capture_create,
+        ),
+    ):
+        async with Client(mcp_server) as client:
+            await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "metrics": ["count"],
+                        "filters": [
+                            {"col": "category", "op": "==", "val": "Electronics"}
+                        ],
+                    }
+                },
+            )
+
+    assert len(captured_queries) == 1
+    filters = captured_queries[0]["filters"]
+    assert len(filters) == 1
+    assert filters[0]["col"] == "category"
+    assert filters[0]["op"] == "=="
+    assert filters[0]["val"] == "Electronics"
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_empty_results(mcp_server: FastMCP) -> None:
+    """Query that returns no data gives a response with row_count=0."""
+    dataset = _make_dataset()
+    empty_result = {
+        "queries": [
+            {
+                "data": [],
+                "colnames": [],
+                "rowcount": 0,
+                "is_cached": False,
+                "cached_dttm": None,
+                "cache_timeout": 300,
+            }
+        ]
+    }
+
+    with (
+        patch.object(
+            query_dataset_module,
+            "_resolve_dataset",
+            return_value=dataset,
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.validate",
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.run",
+            return_value=empty_result,
+        ),
+        patch(
+            "superset.common.query_context_factory.QueryContextFactory.create",
+            return_value=MagicMock(),
+        ),
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "metrics": ["count"],
+                    }
+                },
+            )
+
+    data = json.loads(result.content[0].text)
+    assert data["row_count"] == 0
+    assert data["data"] == []
+    assert "no data" in data["summary"].lower()
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_by_uuid(mcp_server: FastMCP) -> None:
+    """UUID-based lookup works."""
+    dataset = _make_dataset()
+    result_data = _mock_command_result()
+
+    with (
+        patch.object(
+            query_dataset_module,
+            "_resolve_dataset",
+            return_value=dataset,
+        ) as mock_resolve,
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.validate",
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.run",
+            return_value=result_data,
+        ),
+        patch(
+            "superset.common.query_context_factory.QueryContextFactory.create",
+            return_value=MagicMock(),
+        ),
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": "a1b2c3d4-5678-90ab-cdef-1234567890ab",
+                        "metrics": ["count"],
+                    }
+                },
+            )
+
+    # Verify the resolve function was called with the UUID
+    mock_resolve.assert_called_once()
+    call_args = mock_resolve.call_args
+    assert call_args[0][0] == "a1b2c3d4-5678-90ab-cdef-1234567890ab"
+
+    data = json.loads(result.content[0].text)
+    assert data["dataset_id"] == 1
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_permission_denied(mcp_server: FastMCP) -> None:
+    """Permission denied from ChartDataCommand.validate() returns error."""
+    from superset.errors import ErrorLevel, SupersetError, SupersetErrorType
+    from superset.exceptions import SupersetSecurityException
+
+    dataset = _make_dataset()
+
+    with (
+        patch.object(
+            query_dataset_module,
+            "_resolve_dataset",
+            return_value=dataset,
+        ),
+        patch(
+            "superset.common.query_context_factory.QueryContextFactory.create",
+            return_value=MagicMock(),
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.validate",
+            side_effect=SupersetSecurityException(
+                SupersetError(
+                    message="Access denied",
+                    error_type=SupersetErrorType.DATASOURCE_SECURITY_ACCESS_ERROR,
+                    level=ErrorLevel.WARNING,
+                )
+            ),
+        ),
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "metrics": ["count"],
+                    }
+                },
+            )
+
+    data = json.loads(result.content[0].text)
+    assert data["error_type"] == "QueryError"
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_order_by_valid(mcp_server: FastMCP) -> None:
+    """order_by with valid column/metric names passes through."""
+    dataset = _make_dataset()
+    result_data = _mock_command_result()
+    captured_queries: list[dict[str, Any]] = []
+
+    def capture_create(**kwargs):
+        captured_queries.extend(kwargs.get("queries", []))
+        return MagicMock()
+
+    with (
+        patch.object(
+            query_dataset_module,
+            "_resolve_dataset",
+            return_value=dataset,
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.validate",
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.run",
+            return_value=result_data,
+        ),
+        patch(
+            "superset.common.query_context_factory.QueryContextFactory.create",
+            side_effect=capture_create,
+        ),
+    ):
+        async with Client(mcp_server) as client:
+            await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "metrics": ["count"],
+                        "columns": ["category"],
+                        "order_by": ["count"],
+                        "order_desc": True,
+                    }
+                },
+            )
+
+    assert len(captured_queries) == 1
+    orderby = captured_queries[0].get("orderby", [])
+    assert len(orderby) == 1
+    assert orderby[0][0] == "count"
+    # order_desc=True -> ascending=False
+    assert orderby[0][1] is False
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_order_by_invalid(mcp_server: FastMCP) -> None:
+    """order_by with an unknown name returns validation error."""
+    dataset = _make_dataset()
+
+    with patch.object(
+        query_dataset_module,
+        "_resolve_dataset",
+        return_value=dataset,
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "metrics": ["count"],
+                        "order_by": ["nonexistent"],
+                    }
+                },
+            )
+
+    data = json.loads(result.content[0].text)
+    assert data["error_type"] == "ValidationError"
+    assert "nonexistent" in data["error"]
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_time_column_override(mcp_server: FastMCP) -> None:
+    """Explicit time_column overrides dataset main_dttm_col."""
+    dataset = _make_dataset(main_dttm_col="order_date")
+    result_data = _mock_command_result()
+    captured_queries: list[dict[str, Any]] = []
+
+    def capture_create(**kwargs):
+        captured_queries.extend(kwargs.get("queries", []))
+        return MagicMock()
+
+    with (
+        patch.object(
+            query_dataset_module,
+            "_resolve_dataset",
+            return_value=dataset,
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.validate",
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.run",
+            return_value=result_data,
+        ),
+        patch(
+            "superset.common.query_context_factory.QueryContextFactory.create",
+            side_effect=capture_create,
+        ),
+    ):
+        async with Client(mcp_server) as client:
+            await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "metrics": ["count"],
+                        "time_range": "Last 30 days",
+                        "time_column": "order_date",
+                    }
+                },
+            )
+
+    assert len(captured_queries) == 1
+    query_dict = captured_queries[0]
+    assert query_dict["granularity"] == "order_date"
+    temporal_filters = [f for f in query_dict["filters"] if f["op"] == "TEMPORAL_RANGE"]
+    assert temporal_filters[0]["col"] == "order_date"
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_non_dttm_time_column_warns(mcp_server: FastMCP) -> None:
+    """Using a non-datetime column for time_range produces a warning."""
+    dataset = _make_dataset(main_dttm_col=None)
+    result_data = _mock_command_result()
+
+    with (
+        patch.object(
+            query_dataset_module,
+            "_resolve_dataset",
+            return_value=dataset,
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.validate",
+        ),
+        patch(
+            "superset.commands.chart.data.get_data_command.ChartDataCommand.run",
+            return_value=result_data,
+        ),
+        patch(
+            "superset.common.query_context_factory.QueryContextFactory.create",
+            return_value=MagicMock(),
+        ),
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "metrics": ["count"],
+                        "time_range": "Last 7 days",
+                        "time_column": "category",
+                    }
+                },
+            )
+
+    data = json.loads(result.content[0].text)
+    assert len(data["warnings"]) > 0
+    assert "not marked as a datetime" in data["warnings"][0]
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_invalid_filter_column(mcp_server: FastMCP) -> None:
+    """Filter on a column that doesn't exist returns validation error."""
+    dataset = _make_dataset()
+
+    with patch.object(
+        query_dataset_module,
+        "_resolve_dataset",
+        return_value=dataset,
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        "metrics": ["count"],
+                        "filters": [
+                            {
+                                "col": "nonexistent",
+                                "op": "==",
+                                "val": "test",
+                            }
+                        ],
+                    }
+                },
+            )
+
+    data = json.loads(result.content[0].text)
+    assert data["error_type"] == "ValidationError"
+    assert "nonexistent" in data["error"]
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_metadata_access_denied_no_suggestions(
+    mcp_server: FastMCP,
+) -> None:
+    """Users without data-model metadata access cannot probe column/metric names.
+
+    The privacy gate must fire before the validation step that returns close-match
+    suggestions, so restricted users cannot enumerate schema details via typos.
+    """
+    dataset = _make_dataset()
+
+    with (
+        patch.object(
+            query_dataset_module,
+            "_resolve_dataset",
+            return_value=dataset,
+        ),
+        patch.object(
+            query_dataset_module,
+            "user_can_view_data_model_metadata",
+            return_value=False,
+        ),
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        "dataset_id": 1,
+                        # Typo that would normally trigger close-match suggestions
+                        "metrics": ["countt"],
+                    }
+                },
+            )
+
+    data = json.loads(result.content[0].text)
+    # Must be denied before returning any schema suggestions
+    assert data["error_type"] == "DataModelMetadataRestricted"
+    # Must NOT contain column/metric name suggestions
+    assert "countt" not in data.get("error", "")
+    assert "count" not in data.get("error", "")
+
+
+@pytest.mark.asyncio
+async def test_query_dataset_metadata_access_denied_nonexistent_dataset(
+    mcp_server: FastMCP,
+) -> None:
+    """Metadata-restricted users must not be able to probe dataset existence.
+
+    The privacy gate fires before the DAO lookup, so a restricted caller
+    always receives DataModelMetadataRestricted — never NotFound — regardless
+    of whether the requested dataset ID exists.
+    """
+    with patch.object(
+        query_dataset_module,
+        "user_can_view_data_model_metadata",
+        return_value=False,
+    ):
+        async with Client(mcp_server) as client:
+            result = await client.call_tool(
+                "query_dataset",
+                {
+                    "request": {
+                        # Use a dataset_id that does not exist
+                        "dataset_id": 999999,
+                        "metrics": ["count"],
+                    }
+                },
+            )
+
+    data = json.loads(result.content[0].text)
+    # Must receive restricted error, not a NotFound that leaks existence
+    assert data["error_type"] == "DataModelMetadataRestricted"
+    assert data["error_type"] != "NotFound"

tests/unit_tests/mcp_service/test_auth_user_resolution.py

@@ -372,6 +372,43 @@ def test_mcp_auth_hook_preserves_g_user_in_request_context(app) -> None:
     assert result == "middleware_user"
 
 
+def test_mcp_auth_hook_removes_stale_db_session_in_sync_wrapper(app) -> None:
+    """sync_wrapper calls db.session.remove() BEFORE get_user_from_request().
+
+    Thread pool workers reuse threads across requests; db.session is
+    thread-local and may be bound to a different tenant's DB engine from a
+    prior request. Removing it before user lookup ensures a fresh session is
+    created for the current request.
+
+    The ordering is critical: if remove() were called after user lookup,
+    the stale session binding would already have caused a mismatch error.
+    """
+    fresh_user = _make_mock_user("fresh")
+
+    def dummy_tool():
+        """Dummy tool."""
+        return g.user.username
+
+    wrapped = mcp_auth_hook(dummy_tool)
+
+    with app.test_request_context():
+        g.user = fresh_user
+        with patch("superset.extensions.db") as mock_db:
+
+            def _assert_remove_already_called() -> MagicMock:
+                """Verify remove() was called before user resolution runs."""
+                mock_db.session.remove.assert_called_once_with()
+                return fresh_user
+
+            with patch(
+                "superset.mcp_service.auth.get_user_from_request",
+                side_effect=_assert_remove_already_called,
+            ):
+                result = wrapped()
+
+    assert result == "fresh"
+
+
 # -- default_user_resolver --