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uzaysozen

imdb-mcp-server

get_most_popular_tv_shows

Retrieve popular TV shows from IMDb using pagination to explore trending series in batches of five.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function decorated with @mcp.tool() implements the tool logic: fetches data from IMDb's most-popular-tv endpoint using make_imdb_request, handles empty response, paginates the result with paginated_response, and returns JSON.
    @mcp.tool()
    async def get_most_popular_tv_shows(start: int, ctx: Context) -> str:
        """Get the most popular TV shows from IMDb with pagination.
        Args:
            start: The starting index (0-based) to retrieve TV shows from.
        Returns:
            JSON object containing 5 most popular TV shows starting from the specified index.
        """
        most_popular_tv_shows_url = f"{BASE_URL}/most-popular-tv"
        most_popular_tv_shows_data = await make_imdb_request(most_popular_tv_shows_url, {}, ctx)
        if not most_popular_tv_shows_data:
            return "Unable to fetch most popular TV shows data."
        return json.dumps(paginated_response(most_popular_tv_shows_data, start, len(most_popular_tv_shows_data)), indent=4)
  • Calls register_tools(server) to register all tools including get_most_popular_tv_shows during server creation.
    # Register all tools with the server
    register_tools(server)
  • Calls register_tools(server) to register all tools including get_most_popular_tv_shows in stdio mode.
    register_tools(server)
  • Helper function used by the tool to make authenticated requests to the IMDb API with caching.
    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 format paginated responses with 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
        }
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool fetches from IMDb, uses pagination, and returns exactly 5 items per call. However, it doesn't mention rate limits, authentication needs, data freshness, or what happens with invalid 'start' values. The description adds value but leaves gaps in behavioral understanding.

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, parameter explanation, and return value. It's front-loaded with the core functionality. The 'Args:' and 'Returns:' sections are slightly redundant with schema/output_schema but help readability. Minor trimming could remove the section headers.

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 an output schema present, the description does well: it explains the parameter's semantics and return format. However, for a tool with no annotations, it could better address behavioral aspects like error handling or data source limitations. The 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.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so the description must compensate. It clearly explains that 'start' is a '0-based starting index' for pagination, which adds crucial meaning beyond the schema's bare 'integer' type. However, it doesn't specify valid ranges or what happens at the end of the list, leaving some ambiguity.

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 ('most popular TV shows from IMDb'), and scope ('with pagination'). It distinguishes itself from siblings like 'get_top_250_tv_shows' by focusing on current popularity rather than all-time rankings, and from 'search_imdb' by being a curated list rather than a search query.

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 through the mention of pagination, suggesting this is for browsing popular shows in batches. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_top_250_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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