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uzaysozen

imdb-mcp-server

get_most_popular_movies

Retrieve popular movies from IMDb with pagination. Specify a starting index to get 5 movies in JSON format.

Instructions

Get the most popular movies from IMDb with pagination. Args: start: The starting index (0-based) to retrieve movies from. Returns: JSON object containing 5 most popular movies starting from the specified index.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The core handler function decorated with @mcp.tool() that fetches most popular movies from IMDb API endpoint, handles errors, paginates results, and returns JSON.
    @mcp.tool()
    async def get_most_popular_movies(start: int, ctx: Context) -> str:
        """Get the most popular movies from IMDb with pagination.
        Args:
            start: The starting index (0-based) to retrieve movies from.
        Returns:
            JSON object containing 5 most popular movies starting from the specified index.
        """
        most_popular_movies_url = f"{BASE_URL}/most-popular-movies"
        most_popular_movies_data = await make_imdb_request(most_popular_movies_url, {}, ctx) 
        if not most_popular_movies_data:
            return "Unable to fetch most popular movies data."
        return json.dumps(paginated_response(most_popular_movies_data, start, len(most_popular_movies_data)), indent=4)
  • Helper utility function used by the tool to format the API response into a standard paginated JSON structure with 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
        }
  • Core helper function that makes HTTP requests to the IMDb RapidAPI, handles caching, API key management, 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}")
  • Server creation and tool registration via register_tools(server), which defines and registers the get_most_popular_movies handler among others.
    # Create your FastMCP server as usual
    server = FastMCP("IMDb MCP Server")
    
    # Register all tools with the server
    register_tools(server)
    
    return server
  • Pydantic schema for server configuration, providing the required rapidApiKeyImdb for API requests.
    class ConfigSchema(BaseModel):
        rapidApiKeyImdb: str = Field(..., description="RapidAPI API key for accessing the IMDb API")
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses pagination behavior and specifies the return format (JSON with 5 movies), which is helpful. However, it doesn't mention rate limits, authentication needs, data freshness, or error handling, leaving gaps for a tool that likely accesses external data.

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 well-structured with a clear purpose statement followed by Args and Returns sections. It's appropriately sized with no redundant information, though the 'Args' and 'Returns' labels are slightly verbose for a single parameter.

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 an output schema), the description is reasonably complete. It explains the purpose, parameter meaning, and return format. The output schema existence reduces the need to detail return values, but additional behavioral context (e.g., data source reliability) would enhance 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?

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 crucial semantic context beyond the schema's bare 'integer' type. However, it doesn't clarify valid ranges or constraints (e.g., maximum values).

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 action ('Get'), resource ('most popular movies from IMDb'), and includes pagination context. It distinguishes from siblings like 'get_top_250_movies' by specifying 'most popular' rather than 'top rated', though it doesn't explicitly contrast with 'get_most_popular_tv_shows' or other list tools.

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 popular movies with pagination, but provides no explicit guidance on when to use this versus alternatives like 'get_top_250_movies', 'search_imdb', or other sibling tools. The context is clear but lacks comparative guidance.

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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