jmagar/unraid-mcp — Query, monitor, and manage Unraid servers via GraphQL API through MCP tools.

Description and use

**Brief Summary**
jmagar/unraid-mcp provides a GraphQL‑based control plane for Unraid servers, exposing system information, Docker containers, VMs, array/parity status, notifications, plugins, rclone, and live telemetry. By wrapping these capabilities in a standard API, it lets AI assistants and other automation tools interact with a real Unraid environment as if it were a first‑class service.

**Value Proposition**

• **Unified protocol** – A single GraphQL endpoint replaces dozens of ad‑hoc CLI calls, making it easy to query and manipulate Unraid resources programmatically.
• **AI‑ready interface** – The structured schema is ideal for feeding into AI agents (e.g., LangChain, AutoGPT) or Model Context Protocol servers, enabling “talk‑to‑your‑infrastructure” use cases.
• **Extensible telemetry** – Real‑time metrics and event streams let monitoring dashboards or self‑optimising bots react instantly to state changes.

**Practical Adoption Path**

1. **Spin‑up the MCP container** on an existing Unraid node (Docker image is published).
2. **Configure authentication** (API token or TLS) and point your AI‑agent or backend service at the GraphQL URL.
3. **Generate client code** using the introspection schema (Python, JavaScript, Go, etc.) or use the provided CLI/SDK for quick prototyping.
4. **Incrementally replace manual scripts**: start with read‑only queries (system health, container list), then add mutations for actions like starting/stopping Docker containers or toggling array repairs.
5. **Integrate with higher‑level orchestration** (e.g., a Model Context Protocol server) to expose the Unraid capabilities to multiple AI assistants or external tooling.

**Production Readiness**

• **Active maintenance**: last commit on 2026‑06‑23, 83 stars, 32 forks, and a healthy set of topics indicate an engaged community.
• **Mature stack**: built in Python, leverages well‑known GraphQL libraries, and already ships a CLI/SDK, reducing integration effort.
• **Signal strength**: clear API/SDK exposure, comprehensive feature coverage, and recent releases suggest it is ready for pilot deployments in production environments.
• **Remaining checks**: verify the open‑source license compatibility, perform a security audit of the GraphQL layer, and confirm that maintainers have a documented incident‑response process before scaling to mission‑critical workloads.

Overall, jmagar/unraid-mcp is a high‑readiness OSS component that can quickly bring Unraid management into AI‑driven workflows and DevOps pipelines.

Problem

jmagar/unraid-mcp helps connect AI assistants to real tools and data through a standard protocol.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Evidence

Query, monitor, and manage Unraid servers via GraphQL API through MCP tools. Supports system info, Docker, VMs, array/parity, notifications, plugins, rclone, and live telemetry.

Source