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Superset MCP Integration

by aptro

superset_database_list

Retrieve accessible database connections from Apache Superset to manage data sources and configurations for analytics workflows.

Instructions

Get a list of databases from Superset

Makes a request to the /api/v1/database/ endpoint to retrieve all database connections the current user has access to. Results are paginated.

Returns: A dictionary containing database connection information including id, name, and configuration

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • main.py:745-758 (handler)
    The handler function implementing the 'superset_database_list' tool. It is decorated with @mcp.tool() for registration, @requires_auth for authentication check, and @handle_api_errors for error handling. The core logic delegates to make_api_request to GET /api/v1/database/ from Superset API.
    @mcp.tool()
    @requires_auth
    @handle_api_errors
    async def superset_database_list(ctx: Context) -> Dict[str, Any]:
        """
        Get a list of databases from Superset
    
        Makes a request to the /api/v1/database/ endpoint to retrieve all database
        connections the current user has access to. Results are paginated.
    
        Returns:
            A dictionary containing database connection information including id, name, and configuration
        """
        return await make_api_request(ctx, "get", "/api/v1/database/")
  • Core helper function used by superset_database_list (and other tools) to perform authenticated HTTP requests to Superset APIs, handling CSRF tokens, auto-refresh, 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 requiring a valid access token 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
Behavior4/5

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

With no annotations provided, the description carries full burden and adds valuable behavioral context beyond the input schema. It discloses that results are paginated (important for handling large datasets) and that it makes a request to a specific API endpoint (/api/v1/database/). It also mentions access control ('current user has access to'). However, it doesn't specify rate limits, error conditions, or authentication requirements.

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 three sentences that each add value: stating the purpose, explaining the API call and pagination, and describing the return format. It's front-loaded with the core purpose. While efficient, the third sentence about returns could be slightly more detailed given the lack of output schema.

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

Completeness4/5

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

For a zero-parameter read operation with no annotations and no output schema, the description provides good coverage: purpose, API endpoint, pagination behavior, access context, and return format overview. It adequately compensates for the missing structured fields. However, it could benefit from more detail about the return structure or example output.

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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description appropriately doesn't discuss parameters, maintaining focus on the tool's behavior and output. This meets the baseline of 4 for zero-parameter tools, as no additional parameter information is needed.

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 a list of databases from Superset' with the specific verb 'Get' and resource 'databases'. It distinguishes from siblings like 'superset_database_get_by_id' by indicating it retrieves multiple items rather than a single one. However, it doesn't explicitly contrast with other list tools like 'superset_database_schemas' or 'superset_database_get_tables'.

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 by mentioning it retrieves 'all database connections the current user has access to', suggesting it should be used when needing a comprehensive list. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'superset_database_get_by_id' for specific databases or other list tools for different resources. No exclusions or prerequisites are mentioned.

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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