GPTDARR – Sonarr & Radarr MCP Server

GPTDARR is a Model Context Protocol (MCP) server that bridges AI assistants with the popular media management tools Sonarr and Radarr. By exposing a set of well‑defined tools, it allows an LLM to search for, add, and organize TV shows and movies directly within a user’s existing media library. This removes the need for manual API calls or UI interactions, enabling conversational workflows that can be triggered from any MCP‑compatible chat client such as Claude or 5ire.

The server addresses a common pain point for media enthusiasts: keeping a large collection up‑to‑date without navigating complex web interfaces. It translates natural language requests into precise API calls to Sonarr (for TV series) and Radarr (for movies), handling everything from searching titles to selecting quality profiles and target folders. The result is a seamless experience where an assistant can say “Add the latest season of The Office to my library” and have the series automatically discovered, queued, and stored in the correct location.

Key capabilities include:

• Content lookup – A single tool () searches both Sonarr and Radarr for titles, optionally filtering by year. The assistant can present results to the user before any action is taken.
• Series addition – accepts a list of series names and optional years, automatically creating Sonarr entries with the user‑configured quality profile and root folder.
• Movie addition – mirrors this functionality for Radarr, ensuring movies are added with consistent settings.
• Robust logging – Every lookup and add operation is logged, aiding debugging and providing audit trails. Logs are stored in a dedicated folder for easy access.

Typical use cases span from personal media management to enterprise deployments. A user can integrate GPTDARR into a home automation stack, allowing voice assistants or chatbots to enrich their library on demand. Developers can embed the server in custom dashboards, creating a unified interface for media discovery and ingestion that leverages AI to interpret user intent.

What sets GPTDARR apart is its focus on developer ergonomics. The interactive setup wizard captures all necessary Sonarr/Radarr credentials, quality profiles, and storage paths in a single pass. The included system prompt ensures the LLM consistently follows best practices, reducing hallucinations and mis‑directed tool calls. As a result, developers can quickly prototype conversational media workflows with minimal configuration overhead, while users enjoy a frictionless addition of new content to their collections.