TMDB MCP Server
# TMDB MCP Server
This project implements a Model Context Protocol (MCP) server that integrates with The Movie Database (TMDB) API. It enables AI assistants like Claude to interact with movie data, providing capabilities for searching, retrieving details, and generating content related to movies.
## Features
### Resources
- **Static Resources**:
- `tmdb://info` - Information about TMDB API
- `tmdb://trending` - Currently trending movies
- **Resource Templates**:
- `tmdb://movie/{id}` - Detailed information about a specific movie
### Prompts
- **Movie Review**: Generate a customized movie review with specified style and rating
- **Movie Recommendation**: Get personalized movie recommendations based on genres and mood
### Tools
- **Search Movies**: Find movies by title or keywords
- **Get Trending Movies**: Retrieve trending movies for day or week
- **Get Similar Movies**: Find movies similar to a specified movie
## Setup Instructions
### Prerequisites
- Node.js (v16 or later)
- npm or yarn
- TMDB API key
### Installation
1. Clone this repository
```
git clone https://github.com/your-username/tmdb-mcp.git
cd tmdb-mcp
```
2. Install dependencies
```
npm install
```
3. Configure your TMDB API key
- Create a `.env` file in the project root (alternative: edit `src/config.ts` directly)
- Add your TMDB API key: `TMDB_API_KEY=your_api_key_here`
4. Build the project
```
npm run build
```
5. Start the server
```
npm start
```
### Setup for Claude Desktop
1. Open Claude Desktop
2. Go to Settings > Developer tab
3. Click "Edit Config" to open the configuration file
4. Add the following to your configuration:
```json
{
"mcpServers": {
"tmdb-mcp": {
"command": "node",
"args": ["/absolute/path/to/your/tmdb-mcp/build/index.js"]
}
}
}
```
5. Restart Claude Desktop
## Usage Examples
### Using Static Resources
- "What is TMDB?"
- "Show me currently trending movies"
### Using Resource Templates
- "Get details about movie with ID 550" (Fight Club)
- "Tell me about the movie with ID 155" (The Dark Knight)
### Using Prompts
- "Write a detailed review for Inception with a rating of 9/10"
- "Recommend sci-fi movies for a thoughtful mood"
### Using Tools
- "Search for movies about space exploration"
- "What are the trending movies today?"
- "Find movies similar to The Matrix"
## Development
### Project Structure
```
tmdb-mcp/
├── src/
│ ├── index.ts # Main server file
│ ├── config.ts # Configuration and API keys
│ ├── handlers.ts # Request handlers
│ ├── resources.ts # Static resources
│ ├── resource-templates.ts # Dynamic resource templates
│ ├── prompts.ts # Prompt definitions
│ ├── tools.ts # Tool implementations
│ └── tmdb-api.ts # TMDB API wrapper
├── package.json
├── tsconfig.json
└── README.md
```
### Testing
Use the MCP Inspector to test your server during development:
```
npx @modelcontextprotocol/inspector node build/index.js
```
## License
MIT
## Acknowledgements
- [The Movie Database (TMDB)](https://www.themoviedb.org/)
- [Model Context Protocol](https://modelcontextprotocol.github.io/)TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: get-movie-details retrieves specific movie information, get-similar finds related movies, get-trending lists popular movies, and search-movies performs keyword-based queries. There is no overlap in functionality, making tool selection straightforward for an agent.
All tools follow a consistent verb-noun pattern using kebab-case (e.g., get-movie-details, get-similar, get-trending, search-movies). The naming is uniform and predictable, with 'get' for retrieval operations and 'search' for querying, enhancing readability and usability.
With 4 tools, the server is well-scoped for basic movie discovery and information retrieval. While it covers core functions like details, similarity, trends, and search, it might feel slightly thin for a full movie database API, but it is reasonable and focused.
The tool set covers key read operations for movie data, including details, similarity, trends, and search. However, there are notable gaps such as no update, delete, or creation tools (if applicable to the domain), and missing operations for TV shows, actors, or reviews, which could limit agent workflows in broader contexts.