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uzaysozen

imdb-mcp-server

get_top_250_movies

Retrieve IMDb's top 250 movies in paginated format by specifying a starting index to access the ranked list.

Instructions

Get the top 250 movies from IMDb with pagination. Args: start: The starting index (0-based) to retrieve movies from. Returns: JSON object containing 5 top movies starting from the specified index.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the get_top_250_movies MCP tool. It fetches the top 250 movies list from the IMDb API and returns a paginated JSON response of 5 movies starting from the given index.
    @mcp.tool()
    async def get_top_250_movies(start: int, ctx: Context) -> str:
        """Get the top 250 movies from IMDb with pagination.
        Args:
            start: The starting index (0-based) to retrieve movies from.
        Returns:
            JSON object containing 5 top movies starting from the specified index.
        """
        top_250_url = f"{BASE_URL}/top250-movies"
        top_250_data = await make_imdb_request(top_250_url, {}, ctx)
        if not top_250_data:
            return "Unable to fetch top 250 movies data."
        return json.dumps(paginated_response(top_250_data, start, len(top_250_data)), indent=4)
  • Invocation of register_tools(server) which defines and registers the get_top_250_movies tool (and others) using @mcp.tool() decorators inside the function.
    # Register all tools with the server
    register_tools(server)
  • Helper function that performs the HTTP request to the IMDb API endpoint, handles authentication via RapidAPI key, caching, and error handling. Called by the tool handler.
    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}")
  • Utility function to create a standardized paginated response object with 5 items per page, used by the get_top_250_movies handler.
    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 in the tool to construct the API endpoint URL for top 250 movies.
    BASE_URL = "https://imdb236.p.rapidapi.com/api/imdb"
Behavior4/5

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: pagination behavior (5 movies per call, 0-based indexing) and return format (JSON object). However, it doesn't mention rate limits, authentication needs, or error handling.

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?

Perfectly structured with a clear purpose statement followed by Args and Returns sections. Every sentence adds value: the first sets context, the second explains the parameter, the third describes the output. 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 1 parameter with 0% schema coverage and no annotations, the description does well by explaining the parameter and output. However, as a data retrieval tool with no annotations, it could mention rate limits or data freshness. The existence of an output schema reduces the need to detail return values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

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 fully explains the single parameter 'start' as 'The starting index (0-based) to retrieve movies from', adding crucial semantics not in the schema (which only shows type: integer). This completely compensates for the schema gap.

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 'top 250 movies from IMDb', specifying the exact dataset. It distinguishes from siblings like 'get_most_popular_movies' and 'get_top_250_tv_shows' by focusing on IMDb's top 250 movies ranking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for paginated retrieval of IMDb's top 250 movies, but does not explicitly state when to use alternatives like 'get_most_popular_movies' or 'search_imdb'. It provides clear context (pagination) but lacks explicit exclusions or comparisons.

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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