IMDb MCP Server
by uzaysozen
A Python server implementing Model Context Protocol (MCP) for movie and TV show information using the IMDb API service. It serves as a bridge between agents and the IMDb database, offering detailed information about movies, TV shows, actors, directors, and more.
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What is IMDb MCP Server?
This server provides a comprehensive set of tools for accessing IMDb data through the IMDb API. It acts as an intermediary, enabling agents to retrieve detailed information about movies, TV shows, actors, directors, and more.
How to use IMDb MCP Server?
To use this server, you need to obtain an API key from RapidAPI for the IMDb API service. Then, configure the server using either Docker or direct Python execution, ensuring the API key is set as an environment variable. Finally, integrate the server into your application by specifying the command, arguments, and environment in your configuration file.
Key features of IMDb MCP Server
Movie and TV show search capabilities
Detailed information about movies and TV shows
Cast and crew information
Top-rated and popular content lists
Box office data
Country-specific movie information (with special focus on Indian cinema)
Upcoming releases
Efficient response caching system
Use cases of IMDb MCP Server
Retrieving movie details for a chatbot
Searching for TV shows based on genre
Finding the cast and crew of a specific movie
Getting top-rated movies for recommendation systems
FAQ from IMDb MCP Server
How do I get an API key?
How do I get an API key?
Create an account on RapidAPI and subscribe to the IMDb API to obtain an API key.
What are the installation options?
What are the installation options?
You can install the server using Docker or direct Python execution.
How do I start the server?
How do I start the server?
You can start the server directly using python imdb_server.py, using MCP CLI with mcp run imdb_server.py, or if using Docker, the server starts automatically with the container.
What is the pagination system?
What is the pagination system?
The pagination system limits each response to 5 items, preventing overwhelm in AI agents that process the data and allowing structured exploration of large datasets through multiple tool calls.
What is the caching system?
What is the caching system?
The caching system stores API responses in memory for quick retrieval, reducing API usage and improving response times.