Skip to main content
Glama
CW-Codewalnut

Metabase MCP Server

get_card_query_results

Retrieve query results from a Metabase card by providing its ID to access business intelligence data through the MCP server.

Instructions

Get the results of a card's query.

Args: card_id (int): ID of the card.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
card_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for 'get_card_query_results' tool. It's decorated with @mcp.tool() to register it as an MCP tool. The function takes a card_id (int) and makes a POST request to the Metabase API endpoint /api/card/{card_id}/query to retrieve query results.
    @mcp.tool()
    async def get_card_query_results(card_id: int) -> Dict[str, Any]:
        """
        Get the results of a card's query.
        
        Args:
            card_id (int): ID of the card.
            
        Returns:
            Dict[str, Any]: Query result data.
        """
        logger.info(f"Getting query results for card {card_id}")
        return await make_metabase_request(RequestMethod.POST, f"/api/card/{card_id}/query")
  • Initialization of the FastMCP instance named 'metabase' which serves as the MCP server. Tools are registered via the @mcp.tool() decorator.
    # Initialize FastMCP agent
    mcp = FastMCP("metabase", lifespan=app_lifespan)
  • The 'make_metabase_request' helper function that handles all HTTP communication with the Metabase API. It manages session handling, authentication via API key, error handling with custom exceptions, and response parsing.
    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
  • RequestMethod enum defining HTTP methods (GET, POST, PUT, DELETE) used by the tool to specify the request type when calling make_metabase_request.
    from enum import Enum, auto
    
    class RequestMethod(Enum):
        GET = auto()
        POST = auto()
        PUT = auto()
        DELETE = auto()
    
        def __str__(self):
            return self.name
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states this is a 'Get' operation, implying read-only behavior, but doesn't disclose any behavioral traits such as authentication requirements, rate limits, error conditions, or what 'Query result data' entails (e.g., format, size, or pagination). The description is minimal and lacks critical operational context for a tool that likely interacts with a database system.

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 appropriately sized and front-loaded, with the core purpose stated first. The 'Args' and 'Returns' sections are structured but slightly redundant since an output schema exists. Every sentence earns its place, though the 'Returns' line could be omitted given the output schema. It's efficient without being overly terse.

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

Completeness3/5

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

Given the complexity (a query tool with no annotations), 0% schema coverage, but an output schema exists, the description is minimally complete. The output schema handles return values, so the description doesn't need to explain them. However, it lacks context on behavioral aspects (e.g., permissions, errors) and usage guidelines, making it adequate but with clear gaps for effective tool selection.

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?

With 0% schema description coverage, the description must compensate. It adds meaning by explaining that 'card_id' is an 'ID of the card', which clarifies the parameter's role beyond the schema's generic 'integer' type. However, it doesn't provide details like valid ranges, where to find card IDs, or examples. Since there's only one parameter, the description adequately covers its semantics, but more context would be beneficial.

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

Purpose4/5

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

The description clearly states the verb 'Get' and the resource 'results of a card's query', making the purpose understandable. It distinguishes from siblings like 'execute_sql_query' (which runs raw SQL) and 'get_metabase_cards' (which lists cards), but doesn't explicitly contrast with 'get_dashboard_cards' or 'get_dashboard_items' which might retrieve related metadata. The purpose is specific but sibling differentiation could be more explicit.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing card), exclusions (e.g., not for dashboard queries), or direct comparisons to siblings like 'execute_sql_query' or 'get_dashboard_cards'. Usage is implied from the name and purpose alone, with no explicit context provided.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/CW-Codewalnut/metabase-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server