IMDB MCP Server

IMDB MCP Server

Provides access to IMDB movie and person data, including searches, cast details, and top-rated lists through the Model Context Protocol. It enables LLMs to fetch structured cinematic metadata and filmographies using the Cinemagoer backend.

Category
Visit Server

README

IMDB MCP Server

A cloud-runnable JSON-RPC service that provides access to IMDB data. Designed to work with LLMs via the MCP (Model Context Protocol).

How It Works

  1. LLM Configuration: Add the MCP server URL to your LLM's configuration
  2. Discovery: The LLM queries the server URL to understand available tools
  3. Query: The LLM sends JSON-RPC requests to fetch IMDB data
  4. Response: The server returns structured data that the LLM interprets and explains to users

Endpoints

Endpoint Method Description
/ GET HTML documentation (human-readable)
/info GET Service information (JSON)
/jsonrpc POST JSON-RPC 2.0 endpoint
/mcp POST MCP protocol endpoint for LLM tool calling
/health GET Health check for cloud deployments

Available Tools

Tool Description Parameters
search_movies Search movies by title query, limit
get_movie_details Get full movie info movie_id
search_people Search actors/directors query, limit
get_person_details Get person info/filmography person_id
get_top_250_movies Get IMDB Top 250 limit
get_movie_cast Get cast of a movie movie_id, limit

JSON-RPC Usage

Send a POST request to /jsonrpc:

{
    "jsonrpc": "2.0",
    "method": "search_movies",
    "params": {"query": "The Matrix", "limit": 5},
    "id": 1
}

Response:

{
    "jsonrpc": "2.0",
    "result": [
        {"id": "0133093", "title": "The Matrix", "year": 1999},
        {"id": "0234215", "title": "The Matrix Reloaded", "year": 2003}
    ],
    "id": 1
}

LLM MCP Configuration

Add to your LLM's MCP server configuration:

{
    "mcpServers": {
        "imdb": {
            "url": "https://your-deployed-url.com/mcp",
            "transport": "http"
        }
    }
}

Local Development

Run locally

pip install -r requirements.txt
python app.py

Server runs at http://localhost:8080

Run with Docker

docker build -t imdb-mcp .
docker run -p 8080:8080 imdb-mcp

Cloud Deployment

Cloud Foundry

cf push

Uses manifest.yaml configuration.

SAP BTP (Cloud Foundry)

# Build MTA archive
mbt build

# Deploy
cf deploy mta_archives/imdb-mcp-server_1.0.0.mtar

Uses mta.yaml configuration.

Heroku

heroku create imdb-mcp-server
git push heroku main

Uses Procfile and runtime.txt.

Docker (Any Cloud)

# Build
docker build -t imdb-mcp .

# Run
docker run -p 8080:8080 imdb-mcp

# Push to registry
docker tag imdb-mcp your-registry/imdb-mcp:latest
docker push your-registry/imdb-mcp:latest

Google Cloud Run

gcloud run deploy imdb-mcp \
    --source . \
    --platform managed \
    --allow-unauthenticated

AWS App Runner / ECS

Use the Dockerfile with your preferred AWS container service.

Project Structure

imdb-mcp/
├── app.py              # FastAPI JSON-RPC server
├── imdb_backend.py     # Cinemagoer wrapper
├── server.py           # FastMCP server (for local MCP)
├── requirements.txt    # Python dependencies
├── pyproject.toml      # Project metadata
├── Dockerfile          # Container image
├── Procfile            # Heroku/PaaS process file
├── runtime.txt         # Python version
├── manifest.yaml       # Cloud Foundry manifest
├── mta.yaml            # SAP BTP MTA descriptor
├── .dockerignore       # Docker ignore patterns
└── .cfignore           # Cloud Foundry ignore patterns

Environment Variables

Variable Default Description
PORT 8080 Server port

Notes

  • Cinemagoer queries IMDB via web scraping, responses may be slower than API-based solutions
  • For non-commercial use only (per IMDB terms of service)
  • No API key required

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured