Mcparr Server

Overview of the MCP Radarr and Sonarr Server

The MCP Radarr and Sonarr Server bridges the gap between AI assistants and home media automation systems. By exposing a unified Model Context Protocol interface, it lets conversational agents query, control, and monitor media libraries managed by Radarr (movies) and Sonarr (TV shows). Developers can therefore build AI‑powered workflows that read library metadata, trigger downloads, and keep users informed about the health of their media infrastructure—all without writing custom integrations for each platform.

At its core, the server provides a set of high‑level actions that mirror common media‑management tasks. Users can browse the entire collection, filter results by release year or genre, and initiate new downloads with a single command. The system also reports the current download status of any item, allowing assistants to give real‑time feedback (“Your requested movie is still queued”). In addition to media control, the server offers system‑level health checks: disk usage, uptime, and API‑level diagnostics for both Radarr and Sonarr. This holistic view is essential for maintaining a reliable media environment, especially when multiple clients or automated downloaders are involved.

Key capabilities include:

• Unified Search – A single API call retrieves movies or TV shows across both platforms, supporting optional filters for year and genre.
• Download Requests – The assistant can trigger a download by specifying the media type and identifier, delegating the task to Radarr or Sonarr’s queue.
• Status Inquiry – Clients can poll the current state of any media item, enabling status updates or automated cleanup actions.
• System Health Reporting – Comprehensive diagnostics (version, uptime, disk space) for Radarr, Sonarr, or both, ensuring that the underlying services remain operational.
• Resource Exposure – Media items are available as resources with clear URI schemes ( and ), simplifying reference in prompts or tool calls.

Real‑world use cases are plentiful. A smart home assistant could answer questions like “Show me all sci‑fi movies from 2020” and then automatically add a requested film to the download queue. Developers can embed the server into continuous‑integration pipelines that keep media libraries synchronized with external catalogs, or build chatbots that notify users when a new season of their favorite show is available. Because the MCP server abstracts away the intricacies of Radarr and Sonarr APIs, teams can focus on conversational logic rather than protocol plumbing.

In summary, the MCP Radarr and Sonarr Server empowers AI assistants to act as intelligent media managers. By offering a clean, protocol‑agnostic interface for browsing, downloading, and monitoring, it streamlines development of media‑centric applications and unlocks new possibilities for automated content curation in everyday AI workflows.