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Co-authored-by: codeant-ai-for-open-source[bot] <244253245+codeant-ai-for-open-source[bot]@users.noreply.github.com>
193 lines
6.5 KiB
Python
193 lines
6.5 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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Execute SQL MCP Tool
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Tool for executing SQL queries against databases using the unified
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Database.execute() API with RLS, template rendering, and security validation.
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"""
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from __future__ import annotations
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import logging
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from typing import Any
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from fastmcp import Context
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from superset_core.api.types import CacheOptions, QueryOptions, QueryResult, QueryStatus
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from superset_core.mcp import tool
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from superset.errors import ErrorLevel, SupersetError, SupersetErrorType
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from superset.exceptions import SupersetErrorException, SupersetSecurityException
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from superset.mcp_service.sql_lab.schemas import (
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ColumnInfo,
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ExecuteSqlRequest,
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ExecuteSqlResponse,
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StatementInfo,
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)
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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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@tool(tags=["mutate"])
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@parse_request(ExecuteSqlRequest)
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async def execute_sql(request: ExecuteSqlRequest, ctx: Context) -> ExecuteSqlResponse:
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"""Execute SQL query against database using the unified Database.execute() API."""
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await ctx.info(
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"Starting SQL execution: database_id=%s, timeout=%s, limit=%s, schema=%s"
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% (request.database_id, request.timeout, request.limit, request.schema_name)
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)
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# Log SQL query details (truncated for security)
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sql_preview = request.sql[:100] + "..." if len(request.sql) > 100 else request.sql
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await ctx.debug(
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"SQL query details: sql_preview=%r, sql_length=%s, has_template_params=%s"
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% (
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sql_preview,
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len(request.sql),
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bool(request.template_params),
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)
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)
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logger.info("Executing SQL query on database ID: %s", request.database_id)
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try:
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# Import inside function to avoid initialization issues
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from superset import db, security_manager
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from superset.models.core import Database
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# 1. Get database and check access
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database = db.session.query(Database).filter_by(id=request.database_id).first()
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if not database:
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raise SupersetErrorException(
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SupersetError(
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message=f"Database with ID {request.database_id} not found",
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error_type=SupersetErrorType.DATABASE_NOT_FOUND_ERROR,
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level=ErrorLevel.ERROR,
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)
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)
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if not security_manager.can_access_database(database):
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raise SupersetSecurityException(
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SupersetError(
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message=f"Access denied to database {database.database_name}",
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error_type=SupersetErrorType.DATABASE_SECURITY_ACCESS_ERROR,
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level=ErrorLevel.ERROR,
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)
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)
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# 2. Build QueryOptions
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# Caching is enabled by default to reduce database load.
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# force_refresh bypasses cache when user explicitly requests fresh data.
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cache_opts = CacheOptions(force_refresh=True) if request.force_refresh else None
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options = QueryOptions(
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catalog=request.catalog,
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schema=request.schema_name,
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limit=request.limit,
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timeout_seconds=request.timeout,
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template_params=request.template_params,
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dry_run=request.dry_run,
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cache=cache_opts,
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)
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# 3. Execute query
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result = database.execute(request.sql, options)
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# 4. Convert to MCP response format
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response = _convert_to_response(result)
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# Log successful execution
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if response.success:
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await ctx.info(
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"SQL execution completed successfully: rows_returned=%s, "
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"execution_time=%s"
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% (
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response.row_count,
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response.execution_time,
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)
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)
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else:
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await ctx.info(
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"SQL execution failed: error=%s, error_type=%s"
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% (response.error, response.error_type)
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)
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return response
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except Exception as e:
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await ctx.error(
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"SQL execution failed: error=%s, database_id=%s"
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% (
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str(e),
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request.database_id,
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)
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)
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raise
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def _convert_to_response(result: QueryResult) -> ExecuteSqlResponse:
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"""Convert QueryResult to ExecuteSqlResponse."""
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if result.status != QueryStatus.SUCCESS:
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return ExecuteSqlResponse(
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success=False,
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error=result.error_message,
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error_type=result.status.value,
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)
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# Build statement info list
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statements = [
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StatementInfo(
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original_sql=stmt.original_sql,
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executed_sql=stmt.executed_sql,
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row_count=stmt.row_count,
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execution_time_ms=stmt.execution_time_ms,
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)
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for stmt in result.statements
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]
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# Get first statement's data for backward compatibility
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first_stmt = result.statements[0] if result.statements else None
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rows: list[dict[str, Any]] | None = None
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columns: list[ColumnInfo] | None = None
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row_count: int | None = None
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affected_rows: int | None = None
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if first_stmt and first_stmt.data is not None:
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# SELECT query - convert DataFrame
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df = first_stmt.data
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rows = df.to_dict(orient="records")
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columns = [ColumnInfo(name=col, type=str(df[col].dtype)) for col in df.columns]
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row_count = len(df)
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elif first_stmt:
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# DML query
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affected_rows = first_stmt.row_count
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return ExecuteSqlResponse(
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success=True,
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rows=rows,
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columns=columns,
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row_count=row_count,
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affected_rows=affected_rows,
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execution_time=(
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result.total_execution_time_ms / 1000
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if result.total_execution_time_ms is not None
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else None
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),
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statements=statements,
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)
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