Skip to main content
Glama
uzaysozen

imdb-mcp-server

get_trending_telugu_movies

Retrieve trending Telugu movies from IMDb by specifying a starting index to get a list of 5 movies.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function implementing the get_trending_telugu_movies tool. It makes an API request to the IMDb endpoint for trending Telugu movies, handles empty responses, applies pagination using the paginated_response helper, and returns a formatted JSON string.
    @mcp.tool()
    async def get_trending_telugu_movies(start: int, ctx: Context) -> str:
        """Get the trending Telugu movies on IMDb.
        Args:
            start: The starting index (0-based) to retrieve movies from.
        Returns:
            JSON object containing 5 trending Telugu movies starting from the specified index.
        """
        trending_telugu_movies_url = f"{BASE_URL}/india/trending-telugu"
        trending_telugu_movies_data = await make_imdb_request(trending_telugu_movies_url, {}, ctx)
        if not trending_telugu_movies_data:
            return "Unable to fetch trending Telugu movies data."
        return json.dumps(paginated_response(trending_telugu_movies_data, start, len(trending_telugu_movies_data)), indent=4)
  • The registration point where register_tools is called on the FastMCP server instance, which defines and registers the get_trending_telugu_movies tool along with others using the @mcp.tool() decorator.
    # Register all tools with the server
    register_tools(server)
  • Helper utility function used by the tool to create a standardized paginated JSON response structure 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 make authenticated and cached HTTP requests to the IMDb RapidAPI endpoint.
    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 discloses the return format (JSON with 5 movies) and pagination behavior (starting index), but lacks critical behavioral details like rate limits, authentication needs, data freshness, or error handling. For a tool with no annotation coverage, this is insufficient.

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: purpose statement, parameter explanation, and return format. It's front-loaded with the core purpose. Minor improvement could be merging the Args/Returns sections more seamlessly, but overall it's efficient with zero waste.

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 presence of an output schema (which handles return values), the description is reasonably complete. It covers purpose, parameter semantics, and return structure. However, it lacks behavioral context like rate limits or data sources, which would be beneficial despite the output schema.

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 description coverage is 0%, so the description must compensate. It explains the 'start' parameter as 'The starting index (0-based) to retrieve movies from', adding essential meaning beyond the schema's basic integer type. However, it doesn't clarify constraints like valid range or typical usage patterns.

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 action ('Get') and resource ('trending Telugu movies on IMDb'), making the purpose specific. It distinguishes from sibling tools like 'get_top_rated_telugu_movies' by focusing on trending rather than top-rated content, and from 'get_trending_tamil_movies' by specifying Telugu 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 Telugu movies, but provides no explicit guidance on when to use this tool versus alternatives like 'get_top_rated_telugu_movies' or 'get_most_popular_movies'. It mentions the return format but doesn't clarify context or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/uzaysozen/imdb-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server