Skip to main content
Glama
aptro

Superset MCP Integration

by aptro

superset_database_get_by_id

Retrieve detailed configuration information for a specific database connection by its ID from Apache Superset.

Instructions

Get details for a specific database

Makes a request to the /api/v1/database/{id} endpoint to retrieve detailed information about a specific database connection.

Args: database_id: ID of the database to retrieve

Returns: A dictionary with complete database configuration information

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
database_idYes

Implementation Reference

  • main.py:761-777 (handler)
    The handler function implementing the 'superset_database_get_by_id' tool. It makes a GET request to the Superset API endpoint /api/v1/database/{database_id} using the shared make_api_request helper, which handles authentication, CSRF tokens, and auto-refresh.
    @mcp.tool()
    @requires_auth
    @handle_api_errors
    async def superset_database_get_by_id(ctx: Context, database_id: int) -> Dict[str, Any]:
        """
        Get details for a specific database
    
        Makes a request to the /api/v1/database/{id} endpoint to retrieve detailed
        information about a specific database connection.
    
        Args:
            database_id: ID of the database to retrieve
    
        Returns:
            A dictionary with complete database configuration information
        """
        return await make_api_request(ctx, "get", f"/api/v1/database/{database_id}")
  • Core helper function used by the tool to perform authenticated API requests to Superset, handling token refresh, CSRF tokens, and error responses.
    async def make_api_request(
        ctx: Context,
        method: str,
        endpoint: str,
        data: Dict[str, Any] = None,
        params: Dict[str, Any] = None,
        auto_refresh: bool = True,
    ) -> Dict[str, Any]:
        """
        Helper function to make API requests to Superset
    
        Args:
            ctx: MCP context
            method: HTTP method (get, post, put, delete)
            endpoint: API endpoint (without base URL)
            data: Optional JSON payload for POST/PUT requests
            params: Optional query parameters
            auto_refresh: Whether to auto-refresh token on 401
        """
        superset_ctx: SupersetContext = ctx.request_context.lifespan_context
        client = superset_ctx.client
    
        # For non-GET requests, make sure we have a CSRF token
        if method.lower() != "get" and not superset_ctx.csrf_token:
            await get_csrf_token(ctx)
    
        async def make_request() -> httpx.Response:
            headers = {}
    
            # Add CSRF token for non-GET requests
            if method.lower() != "get" and superset_ctx.csrf_token:
                headers["X-CSRFToken"] = superset_ctx.csrf_token
    
            if method.lower() == "get":
                return await client.get(endpoint, params=params)
            elif method.lower() == "post":
                return await client.post(
                    endpoint, json=data, params=params, headers=headers
                )
            elif method.lower() == "put":
                return await client.put(endpoint, json=data, headers=headers)
            elif method.lower() == "delete":
                return await client.delete(endpoint, headers=headers)
            else:
                raise ValueError(f"Unsupported HTTP method: {method}")
    
        # Use auto_refresh if requested
        response = (
            await with_auto_refresh(ctx, make_request)
            if auto_refresh
            else await make_request()
        )
    
        if response.status_code not in [200, 201]:
            return {
                "error": f"API request failed: {response.status_code} - {response.text}"
            }
    
        return response.json()
  • Decorator applied to the handler ensuring authentication is present before execution.
    def requires_auth(
        func: Callable[..., Awaitable[Dict[str, Any]]],
    ) -> Callable[..., Awaitable[Dict[str, Any]]]:
        """Decorator to check authentication before executing a function"""
    
        @wraps(func)
        async def wrapper(ctx: Context, *args, **kwargs) -> Dict[str, Any]:
            superset_ctx: SupersetContext = ctx.request_context.lifespan_context
    
            if not superset_ctx.access_token:
                return {"error": "Not authenticated. Please authenticate first."}
    
            return await func(ctx, *args, **kwargs)
    
        return wrapper
  • Decorator applied to the handler for consistent error handling and reporting.
    def handle_api_errors(
        func: Callable[..., Awaitable[Dict[str, Any]]],
    ) -> Callable[..., Awaitable[Dict[str, Any]]]:
        """Decorator to handle API errors in a consistent way"""
    
        @wraps(func)
        async def wrapper(ctx: Context, *args, **kwargs) -> Dict[str, Any]:
            try:
                return await func(ctx, *args, **kwargs)
            except Exception as e:
                # Extract function name for better error context
                function_name = func.__name__
                return {"error": f"Error in {function_name}: {str(e)}"}
    
        return wrapper
  • main.py:141-144 (registration)
    The FastMCP server instance where all tools, including superset_database_get_by_id, are automatically registered via the @mcp.tool() decorator.
    mcp = FastMCP(
        "superset",
        lifespan=superset_lifespan,
        dependencies=["fastapi", "uvicorn", "python-dotenv", "httpx"],
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the HTTP endpoint ('/api/v1/database/{id}') and return type ('dictionary with complete database configuration information'), which adds useful behavioral context. However, it doesn't mention authentication requirements, error handling, or rate limits, leaving gaps for a read operation.

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

Conciseness5/5

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

The description is appropriately sized and front-loaded: the first sentence states the purpose, followed by implementation details and parameter/return documentation. Every sentence adds value without redundancy, making it efficient and well-structured.

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 and no output schema, the description provides adequate basics (purpose, endpoint, parameter meaning, return type) for a simple read tool. However, it lacks details on authentication, error responses, or data format examples, which would enhance completeness for integration.

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 schema description coverage is 0%, but the description compensates well by explaining the single parameter 'database_id' as 'ID of the database to retrieve.' This adds clear meaning beyond the schema's basic type information. Since there's only one parameter, this is sufficient for effective use.

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's purpose: 'Get details for a specific database' and 'retrieve detailed information about a specific database connection.' This specifies the verb ('get details/retrieve') and resource ('specific database'), but doesn't explicitly differentiate it from sibling tools like 'superset_database_list' or 'superset_database_get_connection' beyond the 'by_id' aspect.

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

Usage Guidelines3/5

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

The description implies usage context through the parameter description ('ID of the database to retrieve'), suggesting this tool is for fetching details of a known database ID. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'superset_database_list' for browsing or 'superset_database_get_connection' for connection-specific info.

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/aptro/superset-mcp'

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