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 committed — your URLs and secrets stay on your machine. Now follow one of the two options below.

Option A — Docker (recommended)

Oraclarr runs as a long-running container that serves MCP over HTTP at http://<host>:7979/mcp. You need three things in one folder: your config.yaml, your .env, and a docker-compose.yml.

Using a stack manager (Dockge, Portainer, etc.): create a new stack, paste the compose below into the editor, and put your config.yaml and .env in the same stack folder. Hit deploy.

Create config.yaml as a real file before you deploy. The compose mounts ./config.yaml into the container. If that file doesn't exist yet, Docker silently creates an empty directory at the mount point and the container exits with IsADirectoryError: Is a directory: '/config/config.yaml'. If you hit that: stop the stack, delete the config.yaml directory in the stack folder, create config.yaml as a file, and redeploy.

Using the command line:

cp docker-compose.example.yml docker-compose.yml
docker compose up -d

Either way, this is the compose file — copy/paste it as-is:

services:
  oraclarr:
    image: ghcr.io/bdog720/oraclarr-mcp:latest
    container_name: oraclarr
    restart: unless-stopped
    ports:
      - "7979:7979"           # left side is the host port — change it if 7979 is taken
    volumes:
      - ./config.yaml:/config/config.yaml:ro
    environment:
      SONARR_KEY: ${SONARR_KEY}
      SONARR_ANIME_KEY: ${SONARR_ANIME_KEY}
      RADARR_KEY: ${RADARR_KEY}
      PROWLARR_KEY: ${PROWLARR_KEY}
      QBIT_USER: ${QBIT_USER}
      QBIT_PASS: ${QBIT_PASS}
      PROFILARR_KEY: ${PROFILARR_KEY}

The keys on the right (${SONARR_KEY} …) are read from your .env file. Then connect Claude Desktop.

Option B — Python (uv)

Run Oraclarr directly, no Docker. Requires Python 3.12+ and uv.

From PyPI (no clone needed): the package is published as oraclarr-mcp. Point ORACLARR_CONFIG at your config.yaml and run it with uvx:

ORACLARR_CONFIG=config.yaml uvx oraclarr-mcp

From a clone (for development):

uv sync
ORACLARR_CONFIG=config.yaml uv run python -m oraclarr_mcp

This speaks MCP over stdio — the client launches the process for you, so you register it in your client's config rather than connecting to a URL:

"oraclarr": {
  "command": "uvx",
  "args": ["oraclarr-mcp"],
  "env": { "ORACLARR_CONFIG": "/absolute/path/to/config.yaml" }
}

(From a clone instead of PyPI, use "command": "uv", "args": ["run", "python", "-m", "oraclarr_mcp"], and add "cwd" pointing at the repo.)

Connect Claude Desktop

(For Option A / Docker. Option B registers itself with the JSON snippet above.)

Use the mcp-remote bridge in claude_desktop_config.json. It runs locally as a Claude Desktop "local MCP server", so it can reach the container on your LAN (requires Node.js / npx):

{
  "mcpServers": {
    "oraclarr": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://<host>:7979/mcp", "--allow-http"]
    }
  }
}

Replace <host> with the IP or hostname of the machine running the container.

Why --allow-http? Recent versions of mcp-remote reject plain http:// URLs unless the host is localhost. Without this flag the bridge exits immediately and Claude Desktop shows the server as "disconnected" — even though the server itself is reachable. The flag is safe here: the traffic stays on your trusted LAN.

Why not the native "Add custom connector"? Custom connectors connect to your server from Anthropic's cloud, not from your machine — so the URL must be a publicly reachable HTTPS endpoint (a http:// LAN/localhost URL is rejected). Pointing it at a homelab box means exposing this unauthenticated read-only service to the internet, which we don't recommend until authentication lands (a later phase). If you want it anyway, put a reverse proxy (Caddy/nginx) or tunnel (Cloudflare Tunnel, Tailscale Funnel) with TLS in front and add your own auth.

Security: the service is unauthenticated read-only MCP. Deploy on a trusted LAN only and do not expose port 7979 to the internet. Authentication is a later phase.

Transport settings (Docker only)

These apply to the Docker container only. The image already sets sensible defaults, so you normally don't touch them — the table is here for reference. The Python path (Option B) always uses stdio and ignores these entirely.

Configuration

Instances are named in config.yaml; type selects the client, so any number of instances of any type (e.g. two Sonarrs) just work. Secrets are referenced as ${ENV_VAR} and pulled from .env: