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uzaysozen

imdb-mcp-server

get_top_rated_tamil_movies

Retrieve top-rated Tamil movies from IMDb. Specify a starting index to get 5 movies from the top 50 list.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function for the 'get_top_rated_tamil_movies' tool, decorated with @mcp.tool() for automatic registration. Fetches top-rated Tamil movies from the IMDb API and returns a paginated JSON response.
    @mcp.tool()
    async def get_top_rated_tamil_movies(start: int, ctx: Context) -> str:
        """Top 50 rated Tamil movies on IMDb.
        Args:
            start: The starting index (0-based) to retrieve movies from.
        Returns:
            JSON object containing 5 top rated Tamil movies starting from the specified index.
        """
        top_rated_tamil_movies_url = f"{BASE_URL}/india/top-rated-tamil-movies"
        top_rated_tamil_movies_data = await make_imdb_request(top_rated_tamil_movies_url, {}, ctx)
        if not top_rated_tamil_movies_data:
            return "Unable to fetch top rated Tamil movies data."
        return json.dumps(paginated_response(top_rated_tamil_movies_data, start, len(top_rated_tamil_movies_data)), indent=4)
  • Core helper function called by the tool to make authenticated and 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 create a standardized paginated response object with 5 items per page.
    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
        }
  • Constant BASE_URL used to construct the API endpoint for top-rated Tamil movies.
    BASE_URL = "https://imdb236.p.rapidapi.com/api/imdb"
  • Calls register_tools(server) which registers the get_top_rated_tamil_movies handler via its @mcp.tool() decorator.
    # Register all tools with the server
    register_tools(server)
Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states the tool returns a JSON object with 5 movies, but doesn't disclose critical details like whether it's read-only, how it handles invalid indices, rate limits, or data freshness. The description is too sparse for a tool with behavioral implications.

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

Conciseness3/5

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

The description is brief and structured with separate sections for the overview, args, and returns, but it's somewhat inefficient. The first sentence states 'Top 50 rated Tamil movies' while the returns mention only 5 movies, creating potential confusion. The information could be more front-loaded and cohesive.

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 the tool has an output schema (which handles return values) and only one parameter with low schema coverage, the description is moderately complete. It covers the basic purpose and parameter meaning but lacks usage guidelines and behavioral details, making it adequate but with clear gaps for effective agent use.

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 has 0% description coverage, but the description compensates by explaining the 'start' parameter as 'The starting index (0-based) to retrieve movies from', adding meaningful context beyond the bare schema. However, it doesn't clarify valid ranges or what happens if the index exceeds available movies, leaving some ambiguity.

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 rated Tamil movies on IMDb' with a specific verb ('retrieve') and resource ('Tamil movies'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_top_rated_indian_movies' or 'get_trending_tamil_movies' beyond the 'top rated' aspect, which prevents 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 like 'get_top_rated_indian_movies' or 'get_trending_tamil_movies'. It mentions the tool returns 5 movies starting from an index, but doesn't explain why one would choose this over other movie-listing tools, leaving usage context unclear.

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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