If you're running a homelab with GPU workloads and Docker containers, this server gives Claude read-only access to the same monitoring data you see in the HomeLab Monitor dashboard. It exposes 12 tools covering live host stats, container memory and VRAM usage, GPU utilization with process attribution, systemd service health, disk treemaps, and historical metrics across all your machines. The server polls your infrastructure over SSH and serves everything through MCP on port 9810, so you can ask an agent which container is holding your GPU or what's filling your disks instead of clicking through tabs. It's read-only by design and requires host-level Docker socket access, so keep it inside your VPN. Useful when you want conversational access to your infrastructure without building observability pipelines.
One page for your whole home lab & AI rig — GPU, containers, services, disks. No agents, no Prometheus/Grafana, no cloud.
Your home lab grew into a couple of machines, a Pi, and a GPU that's mysteriously always busy. HomeLab Monitor gives you one self-hosted page that answers the real questions: which model is holding the GPU, which container is eating RAM, what's filling your disks, and is anything down — across every box over SSH: Linux, a Pi, even Windows. Readable from your phone over the VPN.
Get started
# Grab the compose file and go. No GPU required — the GPU panels just light up when one's present.
curl -fsSLO https://raw.githubusercontent.com/SikamikanikoBG/homelab-monitor/main/docker-compose.yml
docker compose up -d
Open http://<your-host>:9800 and you're done. Full options (from source, GPU toolkit, Windows/WSL2) → Install docs.
🆕 v0.14.0 — a built-in read-only MCP server: connect Claude (or any MCP client) to your homelab and explore it with full dashboard parity, no extra container. Release notes · changelog · MCP docs.
What you get
GPU, demystified — live VRAM/util/power/temp, and which container is holding the card (auto-mapped).
Containers, honestly — health plus RAM and VRAM in separate columns (real resident RAM, not page cache).
systemd services — local or remote, your own units highlighted, failures first.
WizTree-style disk treemaps — scan a filesystem, drill into folders, find the space hogs.
Multi-machine over SSH — paste one key per box; Linux, a Pi, even Windows. No agents, no installs.
Push alerts — Discord and ntfy.sh, edge-triggered so they don't spam.
Open the Hosts tab, paste the hub's auto-generated SSH key onto each remote, and the hub starts polling it — no agents, just SSH + Python 3 (PowerShell on Windows). The hub pipes a small self-contained probe over SSH; nothing persists on the remote.
Set these under environment: in docker-compose.yml (all optional):
Variable
Default
Meaning
SAMPLE_INTERVAL
10
Seconds between samples
RETENTION_DAYS
180
How long history is kept
PRESSURE_FREE_MB
2048
Free VRAM below this counts as "pressure"
PORT
9800
Dashboard port
MCP_PORT
9810
Port for the built-in read-only MCP server
ENABLE_MCP
1
Set 0 to run the dashboard without the MCP server
WATCH_CONTAINERS
—
Extra containers to scan for OOM (comma-separated)
WATCH_SERVICES
—
systemd units to always show, even vendor ones (comma-separated)
CHECK_UPDATES
true
Set false to disable the daily GitHub-releases check (no outbound calls)
History lives in ./data/gpu.db (a bind mount), so it survives restarts and upgrades. Alerts, the systemd D-Bus mount, and per-server tuning → Configuration docs.
Under the hood
The hub stitches nvidia-smi, the Docker API, model-server APIs (Ollama, vLLM, llama.cpp, A1111, …), systemd D-Bus, and /proc + /sys into one sampled view, persisted to SQLite and downsampled on read so a six-month range loads as fast as the last hour. Single page, vendored Chart.js, no build step.
The full data pipeline + caller attribution → How it works
Connect an AI agent (MCP)
Your homelab is now legible to AI agents — point a client at one URL and it can see every host, container, GPU and disk. Read-only, no extra setup.
HomeLab Monitor isn't just a dashboard for you anymore; it's context for your AI agent too. A read-only MCP server is built into the same container (served on :9810) — so Claude, Claude Code, or any MCP client connects in one line and explores your whole lab through 12 named tools, with the same coverage you see on the dashboard: hosts, containers, systemd services, GPU and who's driving it, per-process RAM, AI model servers, disk treemaps, history and alerts.
Connect any MCP client — Claude, ChatGPT, or an agent on your own local Ollama models — and it reads your homelab's live state. Read-only: both directions are just question and answer.
# the dashboard is on :9800; the MCP server rides along on :9810
claude mcp add --transport http homelab http://YOUR-HUB:9810/mcp
Once connected, skip the tab-hunting and just ask — the agent picks the right tools:
"My GPU's been pinned for an hour — which model server is loaded, and who's actually calling it?"
"What's eating /backup? Give me the biggest folders and flag anything that looks like runaway logs."
"Which host is lowest on RAM right now, and what's the top process holding it?"
"I want to reboot and run an OS upgrade this weekend — which box needs it most, and what's a safe order given what's running on each?"
Read-only by design — there are no write tools, so an agent can look but never touch your fleet. Turn it off anytime with ENABLE_MCP=0. Full tool list & setup → MCP docs.
Security
This is a host monitor: it runs with host access and a read-only Docker socket, root mount, and D-Bus socket — a broad footprint by design. Keep it behind your LAN/VPN/firewall and don't expose it to the public internet. Details → docs.
⭐ Support the project
If HomeLab Monitor saves you a browser tab or two, a ⭐ on GitHub genuinely helps other home-labbers find it. Thank you!
Contributing
Issues and PRs are very welcome — especially new model-server probes, new monitors, and GPU back-ends. This is a hobby tool meant to help fellow home-labbers, so be kind. See CONTRIBUTING.md.
A lightweight service that enables AI assistants to execute AWS CLI commands (in safe containerized environment) through the Model Context Protocol (MCP). Bridges Claude, Cursor, and other MCP-aware AI tools with AWS CLI for enhanced cloud infrastructure management.
