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CW-Codewalnut

Metabase MCP Server

create_metabase_collection

Create a new collection in Metabase to organize dashboards and charts, with options to set name, color, and parent collection structure.

Instructions

Create a new Metabase collection.

Args: name (str): Name of the collection. color (str, optional): Hex color code. parent_id (int, optional): ID of the parent collection.

Returns: Dict[str, Any]: Newly created collection metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
colorNo
parent_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The async handler function that creates a new Metabase collection. Takes collection name, optional color, and optional parent_id parameters. Builds a payload dictionary and calls the make_metabase_request helper to POST to /api/collection endpoint.
    async def create_metabase_collection(name: str, color: Optional[str] = None, parent_id: Optional[int] = None) -> Dict[str, Any]:
        """
        Create a new Metabase collection.
    
        Args:
            name (str): Name of the collection.
            color (str, optional): Hex color code.
            parent_id (int, optional): ID of the parent collection.
    
        Returns:
            Dict[str, Any]: Newly created collection metadata.
        """
        payload = {"name": name}
        if color:
            payload["color"] = color
        if parent_id:
            payload["parent_id"] = parent_id
        logger.info(f"Creating collection '{name}'")
        return await make_metabase_request(RequestMethod.POST, "/api/collection", json=payload)
  • The @mcp.tool() decorator registers the create_metabase_collection function as an MCP tool with the FastMCP framework.
    @mcp.tool()
  • The make_metabase_request helper function handles all HTTP communication with the Metabase API. Manages session creation, request execution, error handling, and response parsing. Used by create_metabase_collection to make the actual API call.
    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
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 'creates' without detailing behavioral aspects. It doesn't mention whether this requires admin permissions, if collections are permanent or deletable, rate limits, or error conditions. The return type is mentioned but not elaborated.

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 for Args and Returns. It's front-loaded with the core purpose, and each sentence adds value. Slightly verbose with 'Dict[str, Any]' but overall efficient.

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 no annotations, 3 parameters with 0% schema coverage, and an output schema exists (though not shown), the description covers basics but lacks depth. It explains parameters and return type but misses behavioral context like permissions, side effects, or error handling that would be crucial for a creation 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 provides clear semantics for all three parameters: name (required), color (optional hex code), and parent_id (optional parent collection ID). This adds meaningful context beyond the bare schema, though it doesn't explain format constraints like color validation.

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 tool creates a new Metabase collection, specifying the verb 'create' and resource 'Metabase collection'. It distinguishes from siblings like 'create_metabase_card' or 'create_metabase_dashboard' by focusing on collections, but doesn't explicitly contrast with similar creation tools beyond naming.

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?

No guidance is provided on when to use this tool versus alternatives like 'update_metabase_collection' or 'get_metabase_collection'. The description lacks context about prerequisites, permissions needed, or typical workflows involving collection creation.

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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