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CW-Codewalnut

Metabase MCP Server

execute_sql_query

Run SQL queries on Metabase databases to retrieve and analyze data directly through the MCP server interface.

Instructions

Execute a native SQL query through Metabase.

Args: database_id (int): ID of the database to execute the query on. native_query (str): The SQL query to execute. parameters (list, optional): Query parameters.

Returns: Dict[str, Any]: Query execution result.

Notes: - For PostgreSQL databases, column names are be case-sensitive - Use double quotes around column names with mixed case (e.g., "columnName") - Example with quoted column names: SELECT "userId", "orderDate", COUNT(*) FROM "Orders" GROUP BY "userId", "orderDate"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
database_idYes
queryYes
parametersNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for the execute_sql_query tool. It accepts database_id, query string, and optional parameters, constructs a native query payload, and sends it to Metabase's /api/dataset endpoint via the make_metabase_request helper.
    @mcp.tool()
    async def execute_sql_query(
        database_id: int,
        query: str,
        parameters: Optional[List] = None
    ) -> Dict[str, Any]:
        """
        Execute a native SQL query through Metabase.
    
        Args:
            database_id (int): ID of the database to execute the query on.
            native_query (str): The SQL query to execute.
            parameters (list, optional): Query parameters.
    
        Returns:
            Dict[str, Any]: Query execution result.
            
        Notes:
            - For PostgreSQL databases, column names are be case-sensitive
            - Use double quotes around column names with mixed case (e.g., "columnName")
            - Example with quoted column names: 
              SELECT "userId", "orderDate", COUNT(*) FROM "Orders" GROUP BY "userId", "orderDate"
        """
        query_payload = {
            "database": database_id,
            "type": "native",
            "native": {"query": query},
            "parameters": parameters or []
        }
        logger.info(f"Executing SQL query on database {database_id}")
        logger.debug(f"Query: {query[:100]}...")
        return await make_metabase_request(RequestMethod.POST, "/api/dataset", json=query_payload)
  • Initializes the FastMCP server instance. The @mcp.tool() decorator on execute_sql_query registers it as an MCP tool with this server instance.
    # Initialize FastMCP agent
    mcp = FastMCP("metabase", lifespan=app_lifespan)
  • Helper utility function that handles HTTP requests to the Metabase API. Used by execute_sql_query to post the SQL query to the /api/dataset endpoint. Includes error handling, logging, and response validation.
    async def make_metabase_request(
        method: RequestMethod,
        endpoint: str,
        data: Optional[Dict[str, Any] | bytes] = None,
        params: Optional[Dict[str, Any]] = None,
        json: Any = None,
        headers: Optional[Dict[str, str]] = None,
    ) -> Dict[str, Any]:
        """
        Make a request to the Metabase API.
        
        Args:
            method: HTTP method to use (GET, POST, PUT, DELETE)
            endpoint: API endpoint path
            data: Request data (for form data)
            params: URL parameters
            json: JSON request body
            headers: Additional headers
            
        Returns:
            Dict[str, Any]: Response data
            
        Raises:
            MetabaseConnectionError: When the Metabase server is unreachable
            MetabaseResponseError: When Metabase returns a non-2xx status code
            RuntimeError: For other errors
        """
        
        if not METABASE_URL or not METABASE_API_KEY:
            raise RuntimeError("METABASE_URL or METABASE_API_KEY environment variable is not set. Metabase API requests will fail.")
    
        if session is None:
            raise RuntimeError("HTTP session is not initialized. Ensure app_lifespan was called.")
    
        try:
            request_headers = headers or {}
            
            logger.debug(f"Making {method.name} request to {METABASE_URL}{endpoint}")
            
            # Log request payload for debugging (omit sensitive info)
            if json and logger.level <= logging.DEBUG:
                sanitized_json = {**json}
                if 'password' in sanitized_json:
                    sanitized_json['password'] = '********'
                logger.debug(f"Request payload: {sanitized_json}")
                
            response = await session.request(
                method=method.name,
                url=endpoint,
                timeout=aiohttp.ClientTimeout(total=30),
                headers=request_headers,
                data=data,
                params=params,
                json=json,
            )
    
            try:
                # Handle 500 errors with more detailed info
                if response.status >= 500:
                    error_text = await response.text()
                    logger.error(f"Server error {response.status}: {error_text[:200]}")
                    raise MetabaseResponseError(response.status, f"Server Error: {error_text[:200]}", endpoint)
                
                response.raise_for_status()
                response_data = await response.json()
                
                # Ensure the response is a dictionary for FastMCP compatibility
                return ensure_dict_response(response_data)
                
            except aiohttp.ContentTypeError:
                # Handle empty responses or non-JSON responses
                content = await response.text()
                if not content:
                    return {"data": {}}
                logger.warning(f"Received non-JSON response: {content}")
                return {"data": content}
    
        except aiohttp.ClientConnectionError as e:
            logger.error(f"Connection error: {str(e)}")
            raise MetabaseConnectionError("Metabase is unreachable. Is the Metabase server running?") from e
        except aiohttp.ClientResponseError as e:
            logger.error(f"Response error: {e.status}, {e.message}, {e.request_info.url}")
            raise MetabaseResponseError(e.status, e.message, str(e.request_info.url)) from e
        except Exception as e:
            logger.error(f"Request error: {str(e)}")
            raise RuntimeError(f"Request error: {str(e)}") from e
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It discloses important behavioral traits like PostgreSQL case-sensitivity requirements and quoting conventions, but doesn't mention authentication needs, rate limits, transaction behavior, or what happens with invalid queries. The description adds meaningful context but leaves gaps for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (Args, Returns, Notes) and front-loaded purpose. Every sentence adds value, though the PostgreSQL-specific notes might be overly detailed for a general SQL execution tool. The example query is helpful but slightly lengthens the description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given this is a mutation tool with no annotations, 0% schema coverage, but with output schema present, the description does well. It covers purpose, parameters, return type, and important behavioral notes. The main gap is lack of guidance on error handling or security implications for a raw SQL execution tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It clearly explains all three parameters: database_id identifies the target, native_query contains the SQL, and parameters are optional query parameters. The description adds crucial meaning beyond the bare schema, though it could specify parameter format or types more explicitly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Execute a native SQL query') and resource ('through Metabase'), distinguishing it from all sibling tools which focus on CRUD operations for Metabase objects rather than direct SQL execution. The verb+resource combination is precise and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool (executing SQL queries on Metabase databases) and includes specific notes about PostgreSQL case-sensitivity. However, it doesn't explicitly state when NOT to use it or mention alternatives like the 'get_card_query_results' sibling tool for pre-defined queries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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