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uzaysozen

imdb-mcp-server

get_writers

Retrieve the writers for a specific movie using its IMDb ID. This tool provides a JSON object listing all credited writers associated with the film.

Instructions

Get the writers of a movie from IMDb. Args: imdbId: The IMDb ID of the movie to get writers for. Returns: JSON object containing the writers of the movie.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imdb_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The core handler function for the 'get_writers' tool. It constructs the API URL using BASE_URL, calls the make_imdb_request helper to fetch writers data for the given imdb_id, handles empty response, and returns formatted JSON.
    @mcp.tool()
    async def get_writers(imdb_id: str, ctx: Context) -> str:
        """Get the writers of a movie from IMDb.
        Args:
            imdbId: The IMDb ID of the movie to get writers for.
        Returns:
            JSON object containing the writers of the movie.
        """
        writers_url = f"{BASE_URL}/{imdb_id}/writers"
        writers_data = await make_imdb_request(writers_url, {}, ctx)
        if not writers_data:
            return "Unable to fetch writers data for this movie or movie not found."
        return json.dumps(writers_data, indent=4)
  • In the create_server function (used in HTTP mode), the FastMCP server is created and register_tools is called on it, which defines and registers all tools including 'get_writers' via their @mcp.tool() decorators.
    # Create your FastMCP server as usual
    server = FastMCP("IMDb MCP Server")
    
    # Register all tools with the server
    register_tools(server)
  • In stdio mode of main(), the FastMCP server is created and register_tools is called, registering 'get_writers'.
    server = FastMCP("IMDb MCP Server")
    register_tools(server)
  • The helper function make_imdb_request used by get_writers to perform the HTTP request to the IMDb API endpoint, including caching via cache_manager, API key handling from context or env, and error handling.
    async def make_imdb_request(url: str, querystring: dict[str, Any], ctx: Optional[Context] = None) -> Optional[Dict[str, Any]]:
        """Make a request to the IMDb API with proper error handling and caching."""
        
        # Check if it's time to clean the cache
        cache_manager.cleanup_if_needed()
        
        # Create a cache key from the URL and querystring
        cache_key = f"{url}_{str(querystring)}"
        
        # Try to get from cache first
        cached_data = cache_manager.cache.get(cache_key)
        if cached_data:
            return cached_data
        
        # Get API key from session config or fallback to environment variable
        api_key = None
        if ctx and hasattr(ctx, 'session_config') and ctx.session_config:
            api_key = ctx.session_config.rapidApiKeyImdb
        
        if not api_key:
            api_key = os.getenv("RAPID_API_KEY_IMDB")
        
        # Not in cache, make the request
        headers = {
            "x-rapidapi-key": api_key,
            "x-rapidapi-host": "imdb236.p.rapidapi.com",
        }
        
        if not api_key:
            raise ValueError("API key not found. Please set the RAPID_API_KEY_IMDB environment variable or provide rapidApiKeyImdb in the request.")
        
        try:
            response = requests.get(url, headers=headers, params=querystring, timeout=30.0)
            response.raise_for_status()
            data = response.json()
            
            # Cache the response
            cache_manager.cache.set(cache_key, data)
                
            return data
        except Exception as e:
            raise ValueError(f"Unable to fetch data from IMDb. Please try again later. Error: {e}")
  • The BASE_URL constant used in get_writers to construct the writers endpoint URL.
    BASE_URL = "https://imdb236.p.rapidapi.com/api/imdb"
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 of behavioral disclosure. It states the tool retrieves data ('Get') and returns a JSON object, but lacks details on error handling, rate limits, authentication needs, or whether it's a read-only operation. For a tool with no 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.

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 core purpose, followed by clear sections for Args and Returns. Every sentence earns its place without redundancy, making it easy to scan and understand quickly.

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?

Given the tool's low complexity (1 parameter) and the presence of an output schema (which handles return values), the description is largely complete. It covers the purpose, parameter meaning, and return format. However, it lacks behavioral context like error cases or usage guidelines, which slightly reduces completeness for a tool with no annotations.

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 beyond the input schema. The schema has 0% description coverage and only lists 'imdb_id' as a string parameter. The description explains that 'imdbId' is 'The IMDb ID of the movie to get writers for,' clarifying its purpose and format. This compensates well for the low schema coverage, though it doesn't detail exact ID formats or examples.

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 the writers of a movie from IMDb.' It specifies the verb ('Get') and resource ('writers of a movie'), and distinguishes it from siblings like get_directors or get_cast by focusing on writers. However, it doesn't explicitly differentiate from get_imdb_details, which might also provide writer information, keeping it from a perfect 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 siblings like get_imdb_details (which might return comprehensive data including writers) or search_imdb (for finding movies), nor does it specify prerequisites or exclusions. Usage is implied only by the tool's name and description.

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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