What it does

Oraclarr gives a language model one place to answer the operational "why" questions that normally mean tabbing through six web UIs. It aggregates across your services and returns a single, structured answer without changing anything (it is strictly read-only in this phase).

Covered now: Sonarr, Radarr, Prowlarr, qBittorrent, Tdarr, and Profilarr — including multiple instances of the same type (e.g. a separate anime or 4K Sonarr/Radarr). Every tool fans out across all the instances you define and reports each one separately.

In practice

Once registered, you just ask your MCP client. Oraclarr picks the right tool and answers:

Tools

All read-only and outcome-oriented (not one-per-endpoint, which keeps the model accurate):

Install

There are two ways to run Oraclarr — pick one, you don't need both:

• 🐳 Docker (recommended) — a small always-on container that lives next to your arr stack. Best for homelabs; works great with Portainer, Dockge, Unraid, etc.
• 🐍 Python (uv) — run it directly on your machine with no container. Best for local development, or if you simply don't use Docker.

Both options need the same two files. Make them from the templates in this repo — config.example.yaml (your service URLs) and .env.example (your API keys / passwords):

• If you cloned the repo: cp config.example.yaml config.yaml and cp .env.example .env, then edit them.
• If you're pasting into Dockge/Portainer: open those two example files above, copy their contents into a config.yaml and a .env in your stack folder, and fill in your details.

See Configuration below for exactly what goes in config.yaml. Your config.yaml and .env are never committe

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