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114 lines
3.5 KiB
Python
114 lines
3.5 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""
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Get dataset info FastMCP tool
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This module contains the FastMCP tool for getting detailed information
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about a specific dataset.
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"""
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import logging
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from datetime import datetime, timezone
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from fastmcp import Context
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from superset.mcp_service.app import mcp
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from superset.mcp_service.auth import mcp_auth_hook
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from superset.mcp_service.dataset.schemas import (
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DatasetError,
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DatasetInfo,
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GetDatasetInfoRequest,
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serialize_dataset_object,
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)
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from superset.mcp_service.mcp_core import ModelGetInfoCore
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from superset.mcp_service.utils.schema_utils import parse_request
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logger = logging.getLogger(__name__)
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@mcp.tool
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@mcp_auth_hook
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@parse_request(GetDatasetInfoRequest)
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async def get_dataset_info(
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request: GetDatasetInfoRequest, ctx: Context
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) -> DatasetInfo | DatasetError:
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"""Get dataset metadata by ID or UUID.
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Returns columns, metrics, and schema details.
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"""
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await ctx.info(
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"Retrieving dataset information: identifier=%s" % (request.identifier,)
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)
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await ctx.debug(
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"Metadata cache settings: use_cache=%s refresh_metadata=%s force_refresh=%s"
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% (
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request.use_cache,
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request.refresh_metadata,
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request.force_refresh,
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)
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)
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try:
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from superset.daos.dataset import DatasetDAO
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tool = ModelGetInfoCore(
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dao_class=DatasetDAO,
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output_schema=DatasetInfo,
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error_schema=DatasetError,
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serializer=serialize_dataset_object,
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supports_slug=False, # Datasets don't have slugs
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logger=logger,
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)
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result = tool.run_tool(request.identifier)
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if isinstance(result, DatasetInfo):
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await ctx.info(
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"Dataset information retrieved successfully: "
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"dataset_id=%s, table_name=%s, columns_count=%s, metrics_count=%s"
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% (
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result.id,
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result.table_name,
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len(result.columns) if result.columns else 0,
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len(result.metrics) if result.metrics else 0,
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)
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)
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else:
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await ctx.warning(
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"Dataset retrieval failed: error_type=%s, error=%s"
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% (result.error_type, result.error)
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)
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return result
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except Exception as e:
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await ctx.error(
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"Dataset information retrieval failed: identifier=%s, error=%s, "
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"error_type=%s"
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% (
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request.identifier,
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str(e),
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type(e).__name__,
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)
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)
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return DatasetError(
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error=f"Failed to get dataset info: {str(e)}",
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error_type="InternalError",
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timestamp=datetime.now(timezone.utc),
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)
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