"radarr" matching MCP servers:

MCP server exposing Radarr's v3 REST API as tools, enabling LLMs to read and manage movies, downloads, history, indexers, and more.

MCP server exposing Profilarr's v1 REST API as tools, enabling an LLM to inspect and manage linked databases, connected Radarr/Sonarr instances, backups, jobs, announcements, and system status.

Provides a structured data interface for Servarr apps (Sonarr, Radarr, Prowlarr) enabling health checks, analytics, simulations, backups, and guarded write operations.

A locally-run, read-only MCP server that lets an LLM client diagnose a self-hosted *arr media stack by aggregating across Sonarr, Radarr, Prowlarr, qBittorrent, Tdarr, and Profilarr.

Provides 45 tools for monitoring and managing a Docker-based media stack, including Sonarr, Radarr, and other services.

Integrates with Radarr and Sonarr to manage your media library, enabling search, download requests, status checks, and system health monitoring.

Radar is an open-source Kubernetes observability and diagnostics MCP server. It exposes cluster health, workload diagnosis, logs, events, topology, audit findings, and remediation actions to AI agents through the Model Context Protocol.

Enables Claude Desktop to troubleshoot and manage a self-hosted Plex + Servarr media stack, including diagnosing missing media, monitoring download queues and indexer health, and coordinating operational tasks across Radarr, Sonarr, SABnzbd, qBittorrent, Tautulli, TMDb, Prowlarr, and remote hosts.

Free SSL/TLS scanning and Let's Encrypt certificate issuance (private key stays local), plus certificate-expiry monitoring via one MCP server. Public scan and cert tools need no account.

Analyzes enterprise transformation programs to surface maturity gaps and risk signals, enabling AI agents to identify stalled initiatives and prioritize actions.

Enables users to run deterministic rule checks against their own metrics or HTTP/CSV sources and pull back a short decision brief: only the items that crossed a threshold, each with the number that tripped it and suggested next steps. Users can also record accept/reject/edit feedback so subsequent briefs reflect their own playbook, all without API keys or a live LLM.