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uzaysozen

imdb-mcp-server

get_top_250_tv_shows

Retrieve the top 250 TV shows from IMDb with pagination. Specify a starting index to get 5 shows at a time for efficient data access.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main execution logic for the 'get_top_250_tv_shows' tool. It constructs the API URL, fetches data using the make_imdb_request helper, handles empty responses, paginates the results using paginated_response helper, and returns formatted JSON.
    @mcp.tool()
    async def get_top_250_tv_shows(start: int, ctx: Context) -> str:
        """Get the top 250 TV shows from IMDb with pagination.
        Args:
            start: The starting index (0-based) to retrieve TV shows from.
        Returns:
            JSON object containing 5 top TV shows starting from the specified index.
        """
        top_250_tv_shows_url = f"{BASE_URL}/top250-tv"
        top_250_tv_shows_data = await make_imdb_request(top_250_tv_shows_url, {}, ctx)
        if not top_250_tv_shows_data:
            return "Unable to fetch top 250 TV shows data."
        return json.dumps(paginated_response(top_250_tv_shows_data, start, len(top_250_tv_shows_data)), indent=4)
  • Supporting utility function used by the tool to create a standardized paginated JSON response structure with a fixed page size of 5 items.
    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
        }
  • Key supporting utility that handles API requests to the IMDb backend, including caching via cache_manager, authentication with RapidAPI key, error handling, and response parsing. Called by the handler with the specific top250-tv URL.
    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}")
  • Invocation of register_tools(server) which defines the nested tool functions (including get_top_250_tv_shows) decorated with @mcp.tool() to register them on the FastMCP server instance. Similar call exists at line 61 for stdio mode.
    # Register all tools with the server
    register_tools(server)
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses pagination behavior and that it returns 5 shows per call, which is useful context. However, it doesn't mention rate limits, authentication needs, error handling, or whether the data is cached/real-time, leaving gaps for a read operation.

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 main sentence followed by Args and Returns sections, making it easy to parse. It's appropriately sized with no wasted words, though it could be slightly more concise by integrating the return info into the main sentence.

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 (one parameter), no annotations, and an output schema exists (implied by 'Returns'), the description is mostly complete. It covers purpose, parameter semantics, and return format. However, it lacks details on behavioral aspects like rate limits or data freshness, which would enhance completeness.

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?

The 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 TV shows from,' adding crucial meaning beyond the schema's basic integer type. This is comprehensive for the one 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 specific action ('Get'), resource ('top 250 TV shows from IMDb'), and scope ('with pagination'), distinguishing it from siblings like get_most_popular_tv_shows or get_top_250_movies by specifying it's about IMDb's top-rated shows with pagination support.

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 top TV shows from IMDb with pagination, but it doesn't explicitly state when to use this tool versus alternatives like get_most_popular_tv_shows or search_imdb, nor does it mention any prerequisites or exclusions.

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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