TMDB MCP Server

Overview

The wizzy‑mcp‑tmdb server fills a critical gap for developers building AI assistants that need up‑to‑date, richly structured information about movies, TV shows, and personalities. By exposing The Movie Database (TMDB) as a first‑class MCP service, it allows an assistant to query and retrieve media data through the same standardized protocol used for code execution or other tool calls. This eliminates the need to embed TMDB API logic directly into each client, centralizing authentication, rate‑limiting, and response shaping in a single, well‑tested server.

At its core the MCP server implements three focused tools: , , and . The search tool performs a multi‑entity lookup, returning a unified list of movies, shows, and people that match a user query. The details tool fetches comprehensive metadata for any TMDB entity, with optional appendables such as credits or images to provide richer context. The trending tool surfaces the most popular content within a specified time window, enabling dynamic recommendations or trend analyses. Each tool is wrapped in clear input schemas and consistent output formatting, so an AI assistant can reliably parse results without custom adapters.

Developers benefit from the server’s seamless integration into any MCP‑compatible environment—whether an IDE, a chat client, or a custom workflow engine. The server communicates over stdio, making it trivial to launch as a background process and expose its capabilities via an configuration. Once registered, the assistant can invoke any of the three tools with a single JSON RPC call, receiving structured responses that can be rendered in UI components or fed into downstream reasoning steps. This tight coupling between the assistant’s conversational logic and TMDB data streamlines feature development, reduces duplication of API handling code, and ensures consistent error handling across all clients.

Real‑world use cases abound. A streaming recommendation bot can ask users for preferences, search TMDB, and then present a curated list of titles with cast and crew details. A content discovery app can surface trending movies each day, automatically updating its feed without manual API polling. Even educational assistants can pull biographical data about actors or directors to enrich learning modules. Because the server abstracts away authentication (via a bearer token for the TNL proxy) and rate limits, teams can focus on business logic rather than infrastructure concerns.

What sets this MCP server apart is its lightweight, JavaScript‑based implementation coupled with a comprehensive Jest test suite that covers unit, integration, and error scenarios. The design follows the MCP specification closely, exposing only the most essential tools while keeping the payloads lean and typed. This makes it a dependable backbone for any AI‑driven application that needs authoritative, searchable media data without the overhead of managing separate HTTP clients or SDKs.