Wizzy TMDB
A wrapper for TMDB
wizzy-mcp-tmdb
Project Overview and Purpose
The wizzy-mcp-tmdb project is an MCP (Model Context Protocol) server implemented in JavaScript that provides tools to search and retrieve information from The Movie Database (TMDB). It allows AI clients to access movie, TV show, and person data through a standardized protocol.
Key Features
- Search Movies: Perform multi-search across movies, TV shows, and people using the
search_tmdbtool. - Get Details: Fetch detailed information for specific items using the
get_tmdb_detailstool. - Trending Content: Retrieve trending content across all media types with the
trending_alltool.
Installation
Prerequisites
- Node.js version 18 or higher (required for global fetch support)
- A TMDB API key (Bearer token) from your admin, used with the TNL TMDB proxy (production-api.tnl.one)
Setup
-
Clone the repository and navigate to the project directory.
-
Install dependencies:
npm install -
Set up your TMDB API key as an environment variable:
-
On Windows PowerShell:
$env:TMDB_AUTH_TOKEN="YOUR_TNL_PROXY_BEARER_TOKEN" -
On macOS/Linux:
export TMDB_AUTH_TOKEN="YOUR_TNL_PROXY_BEARER_TOKEN"
-
Usage
Starting the MCP Server
To start the server:
npm start
The server communicates over stdio and should be configured in your MCP-compatible client (e.g., IDE or chat client) with the command node mcp-tmdb-server.js and the TMDB_AUTH_TOKEN environment variable.
MCP Integration Examples
Here are code snippets showing how to integrate with the MCP tools:
Search for Movies
// Example MCP tool call for searching
{
"method": "tools/call",
"params": {
"name": "search_tmdb",
"arguments": {
"query": "dune",
"page": 1,
"language": "en-US",
"include_adult": false
}
}
}
Get Movie Details
// Example MCP tool call for getting details
{
"method": "tools/call",
"params": {
"name": "get_tmdb_details",
"arguments": {
"type": "movie",
"id": 438631,
"append": "credits,images"
}
}
}
Get Trending Content
// Example MCP tool call for trending content
{
"method": "tools/call",
"params": {
"name": "trending_all",
"arguments": {
"time_window": "day",
"page": 1,
"language": "en-US"
}
}
}
MCP Client Integration
Per integrare questo MCP server nel tuo client MCP (come un IDE o un client di chat compatibile), segui questi passi:
-
Installa il pacchetto npm se necessario:
npm install -g wizzy-mcp-tmdb -
Crea o aggiorna il file
mcp.jsonnel tuo client MCP con la seguente configurazione:{ "mcpServers": { "tmdb": { "command": "npx", "args": ["wizzy-mcp-tmdb"], "env": { "TMDB_AUTH_TOKEN": "YOUR_TNL_PROXY_BEARER_TOKEN" }, "alwaysAllow": [ "get_watch_providers", "discover_tv", "discover_by_provider" ] } } }Nota: Il
TMDB_AUTH_TOKENpuò essere impostato a un valore casuale per ora, poiché le chiamate API TMDB sono gratuite e non richiedono autenticazione obbligatoria.
Testing Strategy
The project uses Jest for comprehensive testing, including:
- Unit Tests: Validate individual handler functions, input validation, and response formatting (see
tests/unit/handlers.test.js). - Integration Tests: Test API interactions with mocked responses, error handling, and network failures (see
tests/integration/api.test.js). - Protocol Tests: Ensure MCP protocol compliance, including tool listing and calling (see
tests/protocol/mcp.test.js).
Run the test suite with:
npm test
For watch mode:
npm run test:watch
Project Structure
wizzy-mcp-tmdb/
├── mcp-tmdb-server.js # Main MCP server implementation
├── package.json # Project configuration and dependencies
├── MCP_GUIDE.md # Detailed MCP integration guide
├── babel.config.cjs # Babel configuration for Jest
├── tests/
│ ├── unit/
│ │ └── handlers.test.js # Unit tests for handlers
│ ├── integration/
│ │ └── api.test.js # Integration tests for API calls
│ └── protocol/
│ └── mcp.test.js # MCP protocol compliance tests
└── tests/fixtures/ # Mock data for tests
├── movieDetails.json
├── searchMultiResponse.json
└── trendingAllResponse.json
Contributing
We welcome contributions! Please follow these guidelines:
- Fork the repository.
- Create a feature branch.
- Make your changes and add tests.
- Ensure all tests pass.
- Submit a pull request.
License
This project is licensed under the MIT License. See the LICENSE file for details.
Acknowledgments
- Thanks to The Movie Database (TMDB) for providing the API.
- Built using the Model Context Protocol SDK.
Contact
For questions or support, please open an issue on GitHub.
Servidores relacionados
USDA api
This server allow you to ask questions with way more accurate nutrition facts.
Product Hunt
Discover and search for the latest products and tech using the Product Hunt API.
Nexus
Web search server that integrates Perplexity Sonar models via OpenRouter API for real-time, context-aware search with citations
Meyhem
Agent-native search proxy with feedback-driven ranking. Results ranked by whether agents actually succeed with them.
DuckDuckGo Search
Perform web searches using the DuckDuckGo API, with features for fetching and parsing content.
Google Search Console MCP for Claude Code
Google Search Console MCP for Claude Code & Cursor with built-in SEO intelligence: traffic-drop diagnosis, quick wins, content decay, cannibalization, ranking alerts. Read-only by default, with anti-hallucination provenance metadata on every response.
SkillFlow
AI skills marketplace MCP server - search, discover, and install AI agent skills from SkillFlow.builders marketplace
Knowledge Vault Search
Search a personal knowledge vault using hybrid semantic and keyword matching.
AgentRank
Google for AI agents — live search across 25,000+ scored MCP servers, updated daily
Crawleo MCP Server
Crawleo MCP - Web Search & Crawl for AI Enable AI assistants to access real-time web data through native tool integration. Two Powerful Tools: web.search - Real-time web search with flexible formatting Search from any country/language Device-specific results (desktop, mobile, tablet) Multiple output formats: Enhanced HTML (AI-optimized, clean) Raw HTML (original source) Markdown (formatted text) Plain Text (pure content) Auto-crawl option for full content extraction Multi-page search support web.crawl - Deep content extraction Extract clean content from any URL JavaScript rendering support Markdown conversion Screenshot capture Multi-URL support Features: ✅ Zero data retention (complete privacy) ✅ Real-time, not cached results ✅ AI-optimized with Enhanced HTML mode ✅ Global coverage (any country/language) ✅ Device-specific search (mobile/desktop/tablet) ✅ Flexible output formats (4 options) ✅ Cost-effective (5-10x cheaper than competitors) ✅ Simple Claude Desktop integration Perfect for: Research, content analysis, data extraction, AI agents, RAG pipelines, multi-device testing