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uzaysozen

imdb-mcp-server

get_trending_tamil_movies

Retrieve trending Tamil movies from IMDb using a starting index to access specific results.

Instructions

Get the trending Tamil movies on IMDb. Args: start: The starting index (0-based) to retrieve movies from. Returns: JSON object containing 5 trending Tamil movies starting from the specified index.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Handler function decorated with @mcp.tool() that implements the get_trending_tamil_movies tool. It makes an API request to the IMDb trending Tamil movies endpoint and returns a paginated JSON response.
    @mcp.tool()
    async def get_trending_tamil_movies(start: int, ctx: Context) -> str:
        """Get the trending Tamil movies on IMDb.
        Args:
            start: The starting index (0-based) to retrieve movies from.
        Returns:
            JSON object containing 5 trending Tamil movies starting from the specified index.
        """
        trending_tamil_movies_url = f"{BASE_URL}/india/trending-tamil"
        trending_tamil_movies_data = await make_imdb_request(trending_tamil_movies_url, {}, ctx)
        if not trending_tamil_movies_data:
            return "Unable to fetch trending Tamil movies data."
        return json.dumps(paginated_response(trending_tamil_movies_data, start, len(trending_tamil_movies_data)), indent=4)
  • Calls register_tools(server) within the smithery server creation function, which defines and registers all tools including get_trending_tamil_movies via their decorators.
    # Create your FastMCP server as usual
    server = FastMCP("IMDb MCP Server")
    
    # Register all tools with the server
    register_tools(server)
    
    return server
  • Helper function used by the tool to format paginated responses with a fixed page size of 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
        }
  • Core helper function used by the tool to perform HTTP requests to the IMDb API, handling authentication, caching, and errors.
    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}")
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the return format (JSON object with 5 movies) but doesn't disclose behavioral traits like rate limits, authentication needs, data freshness, or error handling. The description adds some context about the fixed return count but lacks comprehensive behavioral details.

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 front-loaded with the core purpose, followed by structured parameter and return details. Every sentence adds value: the first states what the tool does, the second explains the parameter, and the third describes the return format. No wasted words.

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 (1 parameter, no annotations, but with output schema), the description is reasonably complete. It covers purpose, parameter meaning, and return format. The output schema exists, so detailed return value explanation isn't needed. However, it could benefit from more behavioral context given the lack of 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?

Schema description coverage is 0%, so the description must compensate. It explains the 'start' parameter as 'starting index (0-based) to retrieve movies from', adding meaningful semantics beyond the schema's basic integer type. However, it doesn't clarify valid ranges or constraints for this parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('trending Tamil movies on IMDb'), making the purpose specific. It distinguishes from siblings like 'get_top_rated_tamil_movies' by focusing on trending rather than top-rated movies, and from 'get_trending_telugu_movies' by specifying Tamil language.

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 trending Tamil movies, but doesn't explicitly state when to use this tool versus alternatives like 'get_top_rated_tamil_movies' or 'get_trending_telugu_movies'. No guidance on prerequisites or exclusions is provided.

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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