Amin Ghadersohi 2e7a86f270 feat(mcp): add runtime chart plugin enable/disable via _PluginFilterConfig
Introduces a dynamic filter layer in the chart type registry so operators can
disable individual plugins (e.g. `handlebars`) without a code deploy:

- `MCP_DISABLED_CHART_PLUGINS: frozenset[str]` — static deny-list in mcp_config.py
- `MCP_CHART_PLUGIN_ENABLED_FUNC: Callable[[str], bool] | None` — dynamic hook
  for Harness/Split/per-user targeting; takes precedence over the deny-list
- Both keys are propagated through `get_mcp_config()` defaults

registry.py changes:
- `_PluginFilterConfig` frozen dataclass replaces two bare globals so
  configure() replaces them atomically (no torn reads under concurrency)
- `configure(disabled, enabled_func)` — called at app init; accepts any
  iterable for `disabled`; validates `enabled_func` is callable
- `_is_plugin_enabled()` — reads config once, fails closed on callable exception
- `get()` / `all_types()` / `is_enabled()` apply the filter at lookup time;
  `is_registered()` and `display_name_for_viz_type()` intentionally bypass it
  so callers can distinguish "unknown" vs "disabled" and existing charts still
  resolve display names for disabled viz types

schema_validator.py: two-step pre-check — `is_registered()` for unknown types,
`is_enabled()` for disabled ones, with distinct `DISABLED_CHART_TYPE` error code.

Wiring:
- `SupersetAppInitializer.configure_mcp_chart_registry()` called after
  `configure_feature_flags()` in `init_app()`
- `flask_singleton.py` re-calls `registry.configure()` after the MCP config
  overlay so MCP-specific overrides in `superset_config.py` take effect in
  standalone MCP mode

Tests: 28 cases in test_registry_filters.py covering deny-list, callable hook,
fail-closed on exception, all_types() filtering, display_name bypass, atomic
reconfigure, and configure() with list/tuple/frozenset inputs.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-05-21 10:10:20 +00:00

Superset

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A modern, enterprise-ready business intelligence web application.

Documentation

  • User Guide — For analysts and business users. Explore data, build charts, create dashboards, and connect databases.
  • Administrator Guide — Install, configure, and operate Superset. Covers security, scaling, and database drivers.
  • Developer Guide — Contribute to Superset or build on its REST API and extension framework.

Why Superset? | Supported Databases | Release Notes | Get Involved | Resources | Organizations Using Superset

Why Superset?

Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.

Superset provides:

  • A no-code interface for building charts quickly
  • A powerful, web-based SQL Editor for advanced querying
  • A lightweight semantic layer for quickly defining custom dimensions and metrics
  • Out of the box support for nearly any SQL database or data engine
  • A wide array of beautiful visualizations to showcase your data, ranging from simple bar charts to geospatial visualizations
  • Lightweight, configurable caching layer to help ease database load
  • Highly extensible security roles and authentication options
  • An API for programmatic customization
  • A cloud-native architecture designed from the ground up for scale

Screenshots & Gifs

Video Overview

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Large Gallery of Visualizations


Craft Beautiful, Dynamic Dashboards


No-Code Chart Builder


Powerful SQL Editor


Supported Databases

Superset can query data from any SQL-speaking datastore or data engine (Presto, Trino, Athena, and more) that has a Python DB-API driver and a SQLAlchemy dialect.

Here are some of the major database solutions that are supported:

Amazon Athena   Amazon DynamoDB   Amazon Redshift   Apache Doris   Apache Drill   Apache Druid   Apache Hive   Apache Impala   Apache Kylin   Apache Pinot   Apache Solr   Apache Spark SQL   Ascend   Aurora MySQL (Data API)   Aurora PostgreSQL (Data API)   Azure Data Explorer   Azure Synapse   ClickHouse   Cloudflare D1   CockroachDB   Couchbase   CrateDB   Databend   Databricks   Denodo   Dremio   DuckDB   Elasticsearch   Exasol   Firebird   Firebolt   Google BigQuery   Google Sheets   Greenplum   Hologres   IBM Db2   IBM Netezza Performance Server   MariaDB   Microsoft SQL Server   MonetDB   MongoDB   MotherDuck   OceanBase   Oracle   Presto   RisingWave   SAP HANA   SAP Sybase   Shillelagh   SingleStore   Snowflake   SQLite   StarRocks   Superset meta database   TDengine   Teradata   TimescaleDB   Trino   Vertica   YDB   YugabyteDB

A more comprehensive list of supported databases along with the configuration instructions can be found here.

Want to add support for your datastore or data engine? Read more here about the technical requirements.

Installation and Configuration

Try out Superset's quickstart guide or learn about the options for production deployments.

Get Involved

Contributor Guide

Interested in contributing? Check out our Developer Guide to find resources around contributing along with a detailed guide on how to set up a development environment.

Resources

Understanding the Superset Points of View

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