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CW-Codewalnut

Metabase MCP Server

get_metabase_collection

Retrieve a Metabase collection by ID to access its metadata, enabling users to manage and interact with BI assets through the Metabase MCP Server.

Instructions

Retrieve a single Metabase collection by ID.

Args: collection_id (int): ID of the collection.

Returns: Dict[str, Any]: Collection metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
collection_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function that executes the get_metabase_collection tool logic. It accepts a collection_id parameter and makes a GET request to the Metabase API endpoint /api/collection/{collection_id}.
    async def get_metabase_collection(collection_id: int) -> Dict[str, Any]:
        """
        Retrieve a single Metabase collection by ID.
    
        Args:
            collection_id (int): ID of the collection.
    
        Returns:
            Dict[str, Any]: Collection metadata.
        """
        logger.info(f"Getting collection with ID {collection_id}")
        return await make_metabase_request(RequestMethod.GET, f"/api/collection/{collection_id}")
  • The @mcp.tool() decorator registers the get_metabase_collection function as an MCP tool with the FastMCP framework.
    @mcp.tool()
  • The make_metabase_request helper function that handles HTTP requests to the Metabase API. It manages session handling, error handling, 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
  • The RequestMethod enum used by the handler to specify the HTTP method (GET) for the API 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?

With no annotations provided, the description carries full burden but only states it's a retrieval operation. It doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or whether it's idempotent. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 with a clear purpose statement followed by structured Args and Returns sections. It's front-loaded and wastes no words, though the 'Dict[str, Any]' return type could be slightly more descriptive for non-technical contexts.

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 tool's low complexity (single parameter) and the presence of an output schema (which covers return values), the description is moderately complete. However, it lacks context on permissions, errors, or relationships to sibling tools, leaving room for improvement in guiding the agent.

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?

The description adds meaningful context for the single parameter by specifying it's an 'ID of the collection', which clarifies its purpose beyond the schema's generic 'Collection Id' title. Since schema description coverage is 0%, this compensation is valuable, though it doesn't detail format constraints (e.g., valid ID ranges).

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 'Retrieve' and resource 'a single Metabase collection by ID', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_metabase_cards' or 'get_metabase_dashboards' which retrieve other resources, so it doesn't reach the highest score.

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 sibling tools like 'get_metabase_collections' (if it existed) for listing collections or 'update_metabase_collection' for modifications, leaving usage context implied at best.

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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