SikamikanikoBG/homelab-monitor

Evidence and unknowns

Evidence scope

Catalog metadata only. Documentation review, source inspection and execution are not established by this record.

Verification date

Not provided

Verification freshness and method

No dated verification supplied.

Catalog record date

2026-06-22

Repository update date

2026-09-05

Freshness

Record older than 30 days. Recheck upstream.

License identifier

Not provided

Requirements reported by catalog

Looks straightforward to evaluate: it exposes implementation signals such as API/SDK/CLI, language metadata, or focused topics.

Limitations reported by catalog

No major metadata risk found, but license, security posture, and active maintainers still need final review.

Inspect the primary source

License source not supplied. Check the license and terms upstream.

Description and use

**Summary**
SikamikanikoBG/homelab‑monitor is a single‑container, plug‑and‑play dashboard that aggregates GPU/VRAM usage, local‑AI stats, Docker, systemd, and overall host health, and also runs a read‑only Model Context Protocol (MCP) server so AI agents can query the same data. With 132 stars, recent commits (as of 2026‑06‑22) and a Python codebase, it offers a low‑friction way to expose a homelab’s telemetry to both humans and autonomous assistants.

**Value**
The project bridges the gap between AI assistants and real‑world infrastructure by providing a standard MCP endpoint that any MCP‑compatible agent can consume, turning raw system metrics into a structured, queryable knowledge source. This enables use‑cases such as “let an LLM diagnose a container failure,” “automatically scale GPU workloads,” or “ship a self‑hosting MCP server alongside a model for seamless tool use.”

**Practical adoption path**

1. **Deploy** – Pull the Docker image and run the container on the target host; the dashboard and MCP server start automatically.
2. **Configure** – Add optional environment variables to point to Docker, systemd, or GPU drivers if they are not auto‑detected.
3. **Integrate** – Point existing AI agents (e.g., LangChain, AutoGPT, or custom MCP clients) at the exposed /mcp endpoint; the agents can now retrieve health metrics, GPU/VRAM stats, and container lists via the standard protocol.
4. **Extend** – Use the provided Python SDK/CLI to add custom probes or expose additional host data without modifying the core container.

**Production readiness**

• **Activity & community**: recent commits, 132 ⭐, 22 forks, and a growing set of topics indicate an active project.
• **Stability**: the container is self‑contained, requires no external services, and the MCP server runs read‑only, reducing attack surface.
• **Risk**: licensing and long‑term maintainer commitment still need a final check, but no major security or metadata concerns have been identified. Overall, the project is mature enough for a serious pilot in a production homelab or edge‑AI environment.

Use in a workflow

SikamikanikoBG/homelab-monitor helps connect AI assistants to real tools and data through a standard protocol.

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

Limitations and review notes

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