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uzaysozen

imdb-mcp-server

get_top_rated_malayalam_movies

Retrieve top-rated Malayalam movies from IMDb, returning 5 films starting from a specified index in the list of highest-rated titles.

Instructions

Top 50 Malayalam movies as rated by the IMDb users. Args: start: The starting index (0-based) to retrieve movies from. Returns: JSON object containing 5 top rated Malayalam movies starting from the specified index.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function decorated with @mcp.tool() which defines the tool schema via signature and docstring, registers it, and implements the logic: fetches data from IMDb API endpoint /india/top-rated-malayalam-movies using make_imdb_request helper, extracts items, applies pagination with paginated_response helper, and returns JSON.
    @mcp.tool()
    async def get_top_rated_malayalam_movies(start: int, ctx: Context) -> str:
        """Top 50 Malayalam movies as rated by the IMDb users.
        Args:
            start: The starting index (0-based) to retrieve movies from.
        Returns:
            JSON object containing 5 top rated Malayalam movies starting from the specified index.
        """
        top_rated_malayalam_movies_url = f"{BASE_URL}/india/top-rated-malayalam-movies"
        top_rated_malayalam_movies_data = await make_imdb_request(top_rated_malayalam_movies_url, {}, ctx)
        if not top_rated_malayalam_movies_data:
            return "Unable to fetch top rated Malayalam movies data."
        
        # Use paginated response helper with fixed page size
        movies = top_rated_malayalam_movies_data.get("items", [])
        return json.dumps(paginated_response(movies, start, len(movies)), indent=4)
  • Calls register_tools(server) to execute the registration of all tools, including get_top_rated_malayalam_movies (defined inside register_tools).
    register_tools(server)
  • Core helper function called by the tool handler to make authenticated, cached HTTP requests to the IMDb API.
    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}")
  • Helper function used by the tool to paginate the list of movies with fixed page size 5.
    def paginated_response(items, start, total_count=None):
        """Format a paginated response with a fixed page size of 5."""
        if total_count is None:
            total_count = len(items)
        
        # Validate starting index
        start = max(0, min(total_count - 1 if total_count > 0 else 0, start))
        
        # Fixed page size of 5
        page_size = 5
        end = min(start + page_size, total_count)
        
        return {
            "items": items[start:end],
            "start": start,
            "count": end - start,
            "totalCount": total_count,
            "hasMore": end < total_count,
            "nextStart": end if end < total_count else None
        }
  • BASE_URL constant used to construct the API endpoint URL in the handler.
    BASE_URL = "https://imdb236.p.rapidapi.com/api/imdb"
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool returns '5 top rated Malayalam movies starting from the specified index,' which clarifies pagination behavior (5 items per call) and the 0-based indexing. However, it doesn't address important aspects like rate limits, authentication requirements, error conditions, or whether the data is static or real-time updated.

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 efficiently structured with a clear purpose statement followed by parameter and return value documentation. Every sentence serves a purpose, though the discrepancy between 'Top 50' in the first sentence and '5 top rated' in the returns section creates some confusion that slightly reduces clarity.

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 moderate complexity (single parameter, list retrieval), no annotations, but presence of an output schema, the description provides adequate context. It explains the purpose, parameter usage, and return format. The output schema existence means the description doesn't need to detail return structure, though it could better explain the 'Top 50' versus '5' discrepancy for full completeness.

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 explicitly documents the 'start' parameter as 'The starting index (0-based) to retrieve movies from,' which adds crucial semantic context beyond the schema's bare type declaration (integer). With 0% schema description coverage and only one parameter, this compensation is effective, though it doesn't explain constraints like valid range or maximum index.

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 retrieves 'Top 50 Malayalam movies as rated by IMDb users,' specifying both the resource (Malayalam movies) and the selection criteria (IMDb user ratings). It distinguishes from siblings like get_top_rated_indian_movies by focusing specifically on Malayalam films, though it doesn't explicitly contrast with get_top_rated_tamil_movies or get_top_rated_telugu_movies.

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 for retrieving top-rated Malayalam movies, but provides no explicit guidance on when to use this tool versus alternatives like get_top_rated_indian_movies or get_most_popular_movies. The context is clear (Malayalam movies, IMDb ratings), but lacks specific when/when-not instructions or named alternatives.

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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