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CW-Codewalnut

Metabase MCP Server

delete_metabase_collection

Remove a Metabase collection by its ID to clean up BI assets and organize your data workspace.

Instructions

Delete a Metabase collection.

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

Returns: Dict[str, Any]: Confirmation of the collection deletion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
collection_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler function for 'delete_metabase_collection' tool. Takes collection_id as integer parameter and makes a DELETE request to Metabase API endpoint /api/collection/{collection_id}. Registered with @mcp.tool() decorator. Returns Dict[str, Any] with deletion confirmation.
    @mcp.tool()
    async def delete_metabase_collection(collection_id: int) -> Dict[str, Any]:
        """
        Delete a Metabase collection.
    
        Args:
            collection_id (int): ID of the collection to delete.
    
        Returns:
            Dict[str, Any]: Confirmation of the collection deletion.
        """
        logger.info(f"Deleting collection {collection_id}")
        return await make_metabase_request(RequestMethod.DELETE, f"/api/collection/{collection_id}")
  • Core helper function 'make_metabase_request' that handles HTTP requests to Metabase API. Used by delete_metabase_collection to execute DELETE requests. Manages session, authentication, error handling, and response parsing. Raises MetabaseConnectionError or MetabaseResponseError on failures.
    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 including DELETE, which is used by delete_metabase_collection handler to specify the HTTP DELETE method 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
  • Custom exception classes for Metabase API errors. MetabaseConnectionError raised when server is unreachable, MetabaseResponseError raised for non-2xx status codes. Used by make_metabase_request helper function for error handling in delete_metabase_collection tool.
    class MetabaseRequestError(Exception):
        """Base exception for Metabase API request errors"""
        pass
    
    class MetabaseConnectionError(MetabaseRequestError):
        """Exception raised when Metabase server is unreachable"""
        pass
    
    class MetabaseResponseError(MetabaseRequestError):
        """Exception raised when Metabase returns a non-2xx status code"""
        def __init__(self, status, message, url):
            self.status = status
            self.message = message
            self.url = url
            super().__init__(f"Status {status}, message: '{message}', url: '{url}'")
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 the tool deletes a collection and returns a confirmation, but lacks critical behavioral details: whether deletion is irreversible, permission requirements, effects on nested items, or error handling. For a destructive operation 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 and front-loaded, starting with the core purpose. The Args and Returns sections are structured but slightly verbose for a single parameter; every sentence earns its place by clarifying inputs and outputs efficiently.

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 complexity (destructive operation), lack of annotations, and presence of an output schema (which handles return values), the description is partially complete. It covers the basic action and parameter but misses critical context like safety warnings or usage prerequisites, making it adequate with clear gaps.

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

Parameters3/5

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

Schema description coverage is 0%, but the description compensates by documenting the single parameter 'collection_id' with its type and purpose. However, it doesn't add meaning beyond the schema (e.g., valid ID ranges or sourcing methods). With one parameter and partial compensation, this meets the baseline for minimal viability.

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 action ('Delete') and resource ('a Metabase collection'), making the purpose unambiguous. It distinguishes from siblings like 'get_metabase_collection' or 'update_metabase_collection' by specifying deletion. However, it doesn't explicitly differentiate from other delete tools (e.g., 'delete_metabase_card'), which slightly limits sibling differentiation.

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 collection ID from 'get_metabase_collection'), exclusions (e.g., not for non-existent collections), or comparisons with other deletion tools in the sibling list. This leaves the agent without contextual usage cues.

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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