# radarr-mcp

> radarr-mcp — orellbuehler-radarr-mcp. Use this tool when you need to automate and optimize your movie library management, as it enables AI agents to curate your library, perform indexer searches, and troubleshoot import issues. It takes inputs such as movie metadata and download queue status, and outputs curated library updates and diagnostic reports. Ideal for use cases where manual library management is time-consuming or prone to errors, and automation is required to streamline the process.

Canonical page: https://skillsregistry.net/skills/orellbuehler-radarr-mcp  
JSON: https://api.skillsregistry.net/v1/skills/orellbuehler-radarr-mcp

## Description

MCP server for Radarr — lets AI agents curate the movie library, drive indexer searches, triage the download queue, and diagnose failed imports

## Trust

- **Trust score (0–1):** 0.93
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/OrellBuehler/radarr-mcp)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "orellbuehler-radarr-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/orellbuehler-radarr-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/orellbuehler-radarr-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt