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MCP Radarr and Sonarr Server

MCP Server for Radarr and Sonarr services

#mcparr#media-management
Created by JasonTulp2025/03/28
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What is MCP Radarr and Sonarr Server?

What is MCP Radarr and Sonarr Server? MCP Radarr and Sonarr Server is a media management tool that integrates with Radarr and Sonarr to help users manage their movie and TV show libraries efficiently. How to use MCP Radarr and Sonarr Server? To use the server, you can install it locally or globally. For local installation, run pnpm install, and for global installation, use pnpm run install-global. After installation, configure the environment variables for Radarr and Sonarr, and then run the server using pnpm start or mcparr if installed globally. Key features of MCP Radarr and Sonarr Server? Browse and manage your movie and TV show library. Search and filter media by year and genre. Request downloads for movies and TV shows. Monitor download and system health status, including disk space and health checks. Use cases of MCP Radarr and Sonarr Server? Managing a personal media library for movies and TV shows. Automating download requests for new media content. Monitoring system health and storage for media management. FAQ from MCP Radarr and Sonarr Server? Can I use MCP Server without Radarr and Sonarr? No, MCP Server is designed to work specifically with Radarr and Sonarr for media management. Is there a graphical user interface for MCP Server? Currently, MCP Server operates through command line interface; a GUI may be considered for future updates. How do I check the system health? You can use the get_system_status API endpoint to check the health and storage information of your Radarr and Sonarr instances.

As an MCP (Model Context Protocol) server, MCP Radarr and Sonarr Server enables AI agents to communicate effectively through standardized interfaces. The Model Context Protocol simplifies integration between different AI models and agent systems.

How to use MCP Radarr and Sonarr Server

To use the server, you can install it locally or globally. For local installation, run pnpm install, and for global installation, use pnpm run install-global. After installation, configure the environment variables for Radarr and Sonarr, and then run the server using pnpm start or mcparr if installed globally. Key features of MCP Radarr and Sonarr Server? Browse and manage your movie and TV show library. Search and filter media by year and genre. Request downloads for movies and TV shows. Monitor download and system health status, including disk space and health checks. Use cases of MCP Radarr and Sonarr Server? Managing a personal media library for movies and TV shows. Automating download requests for new media content. Monitoring system health and storage for media management. FAQ from MCP Radarr and Sonarr Server? Can I use MCP Server without Radarr and Sonarr? No, MCP Server is designed to work specifically with Radarr and Sonarr for media management. Is there a graphical user interface for MCP Server? Currently, MCP Server operates through command line interface; a GUI may be considered for future updates. How do I check the system health? You can use the get_system_status API endpoint to check the health and storage information of your Radarr and Sonarr instances.

Learn how to integrate this MCP server with your AI agents and leverage the Model Context Protocol for enhanced capabilities.

Use Cases for this MCP Server

  • No use cases specified.

MCP servers like MCP Radarr and Sonarr Server can be used with various AI models including Claude and other language models to extend their capabilities through the Model Context Protocol.

About Model Context Protocol (MCP)

The Model Context Protocol (MCP) is a standardized way for AI agents to communicate with various services and tools. MCP servers like MCP Radarr and Sonarr Server provide specific capabilities that can be accessed through a consistent interface, making it easier to build powerful AI applications with complex workflows.

Browse the MCP Directory to discover more servers and clients that can enhance your AI agents' capabilities.