TMDB MCP Server Movie Search & Recommendations

Overview

This MCP (Model Context Protocol) server connects to The Movie Database (TMDB) to provide searchable movie metadata and recommendation results in a format useful for model-context retrieval and downstream applications. It acts as a lightweight microservice that converts TMDB responses into MCP-compliant tools and endpoints, so LLMs or other models can query movie data, retrieve details, and obtain context-aware recommendations.

The server is useful when you need a consistent, programmatic interface for movie search and content enrichment — for example, powering a conversational agent that answers movie questions, seeding a recommender pipeline with TMDB metadata, or serving as a catalog lookup microservice for an app. It abstracts TMDB API calls, handles API keys and basic caching, and exposes endpoints suitable for direct use or integration into an MCP-enabled agent.

Features

• Search movies and TV titles using TMDB search endpoints
• Fetch detailed movie metadata (overview, cast, crew, release dates)
• Generate recommendations or related-title suggestions from TMDB
• MCP-friendly endpoints that return compact, context-oriented payloads
• Simple configuration via environment variables
• Example usage with curl and integration-friendly JSON responses

Installation / Configuration

Prerequisites:

• Node.js (LTS recommended) or the runtime indicated in the repository README
• A TMDB API key (register at https://www.themoviedb.org/)

Clone and install:

git clone https://github.com/Laksh-star/mcp-server-tmdb.git
cd mcp-server-tmdb
npm install

Create a .env file in the project root (example):

TMDB_API_KEY=your_tmdb_api_key_here
PORT=3000
CACHE_TTL=3600        # optional: cache time-to-live in seconds
LOG_LEVEL=info        # optional

Start the server:

# production/start
npm start

# development with live reload (if a script exists)
npm run dev

By default the server will bind to the configured PORT (3000 above). If the repository provides additional start scripts (like docker-compose or a Dockerfile), refer to those for containerized deployment.

Available Tools / Resources

• GitHub repository: https://github.com/Laksh-star/mcp-server-tmdb
• TMDB API docs: https://developers.themoviedb.org/3
• Postman / HTTP clients for testing
• (Optional) MCP specification or client library used by your model/agent — adapt the responses to the MCP fields your agent expects

Typical environment variables and configuration options:

Typical Endpoints

Note: Exact paths may vary based on the repository’s implementation — these are representative examples.

Example curl requests:

Search:

curl "http://localhost:3000/search?q=inception&page=1"

Get details:

curl "http://localhost:3000/movie/27205"

Recommendations:

curl "http://localhost:3000/recommendations/27205"

Use Cases

• Chatbot knowledge enrichment: Use the search endpoint to resolve user queries like “who directed Inception?” and provide model-friendly context blocks containing cast and crew.
• Recommendation microservice: Call the recommendations endpoint to fetch related titles for UI displays (e.g., “Users who liked X also liked…”) or to provide candidate prompts for a personalized content feed.
• Catalog augmentation: Enrich an internal catalog’s items with TMDB metadata (overview, genres, poster paths) by querying movie details and merging the results into your database.
• Specialized retrieval for LLMs: Serve compact, MCP-formatted context windows to a language model that needs concise facts (release year, runtime, top-billed cast) without the full TMDB payload.

Tips & Considerations

• Respect TMDB rate limits and caching guidelines; set CACHE_TTL to reduce duplicate requests.
• Keep your TMDB API key secure (do not commit .env to version control).
• Validate incoming query params and sanitize IDs to avoid unnecessary TMDB calls.
• If you plan high throughput, consider adding a persistent cache (Redis) and paginated indexing for large result sets.

For implementation details, endpoint signatures, and any advanced configuration, consult the repository: https://github.com/Laksh-star/mcp-server-tmdb.