# HomeLab Monitor > Self-hosted homelab dashboard with a built-in read-only MCP server (hosts, Docker, GPU, services). - **Type:** MCP server - **Install:** `agentstack add mcp-sikamikanikobg-homelab-monitor` - **Verified:** Pending review - **Seller:** [SikamikanikoBG](https://agentstack.voostack.com/s/sikamikanikobg) - **Installs:** 0 - **Latest version:** 0.14.2 - **License:** MIT - **Upstream author:** [SikamikanikoBG](https://github.com/SikamikanikoBG) - **Source:** https://github.com/SikamikanikoBG/homelab-monitor - **Website:** https://sikamikanikobg.github.io/homelab-monitor/ ## Install ```sh agentstack add mcp-sikamikanikobg-homelab-monitor ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # HomeLab Monitor [](https://github.com/SikamikanikoBG/homelab-monitor/stargazers) [](https://hub.docker.com/r/sikamikaniko123/homelab-monitor) [](https://discord.gg/tpKWKEdSQN) [](CHANGELOG.md) [](https://sikamikanikobg.github.io/homelab-monitor/) **One page for your whole home lab & AI rig — GPU truth (any vendor), tokens/sec, power cost by the hour, uptime, training runs, containers, disks. No agents, no separate metrics stack, no cloud.** Your home lab grew into a couple of machines, a Pi, and a GPU that's mysteriously always busy — and lately it's running models too. HomeLab Monitor gives you one self-hosted page that answers the real questions: **what's that GPU actually doing, which model is holding it, what's it costing you to run, 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 ```bash # 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://:9800` and you're done. Full options (from source, GPU toolkit, Windows/WSL2) → [**Install docs**](https://sikamikanikobg.github.io/homelab-monitor/install/). > 🆕 **v0.21 — see your power bill by the hour, and which GPU is burning it.** A 7×24 **busy-hours cost heatmap** that shows *when* your lab actually costs you money, **any-vendor GPU** support (AMD on Linux with no ROCm; AMD/Intel on Windows — no vendor tools), and **built-in uptime monitoring** for any HTTP/TCP endpoint. [Release notes](https://github.com/SikamikanikoBG/homelab-monitor/releases) · [changelog](CHANGELOG.md). ## What you get One page, every box, the questions you actually have. The classics are all here — and a whole **AI cockpit** builds on top of them. **Your GPU, demystified — and honest about it.** A card pinned at "100% util" can still be throttling, memory-bandwidth-bound, or quietly drooping its clocks. The GPU tab decodes `nvidia-smi`'s throttle reasons (a red banner the moment it's power-capped or too hot), and shows memory-bandwidth util, core/mem clocks, power-vs-limit and p-state — plus *which container is holding the card*. And it's no longer NVIDIA-only: **AMD** GPUs are read on Linux straight from the kernel's `amdgpu` interface (no ROCm), and **AMD and Intel** GPUs on Windows hosts — so your card shows up with its name, utilisation and VRAM, no vendor tools required. **What it costs — down to the process.** Power becomes money: per machine, then per component (GPU measured via `nvidia-smi`, CPU/DRAM via RAPL), then **per process, container or model** — click any row to see what it drew and what it cost over any window. Day & night tariffs (Economy 7, Heures Creuses, …), or just pick your country for a sensible estimate. Every watt is measured or a baseline you set; wall power is never guessed. And a **busy-hours heatmap** turns months of samples into one picture of *when* your lab costs you money — a 7×24 day-of-week × hour grid that shows which hour of the week is priciest at a glance. **Your training runs, priced.** Push a run from Jupyter, Colab or Kaggle with a one-file client (or mirror it from MLflow), and it comes back with the loss curve *and* the real GPU energy it burned, on the same timeline. Create, name, expire and revoke API keys yourself. And the rest of the lab, the way it always was: - **Containers, honestly** — health plus **RAM and VRAM in separate columns** (real resident RAM, not page cache), and click one to tail its logs in a side drawer. - **systemd services** — local or remote, your own units highlighted, failures first. - **WizTree-style disk treemaps**, **network I/O with per-container top talkers**, and a **mini-htop** for who's eating CPU and RAM. - **Multi-machine over SSH** — paste one key per box; Linux, a Pi, even **Windows**. No agents, no installs. - **Uptime monitoring, in the box** — watch any **HTTP endpoint or TCP port** (your services, a NAS, a remote site) straight from the container: heartbeat strip, 24h/7d uptime %, latency, and **smart per-check alerts** — anti-flap confirm, recovery with downtime, and an optional slow-response warning. No extra uptime service to self-host — it's already in the box. - **Push alerts** — **Discord**, **ntfy.sh** and **Telegram**, edge-triggered so they don't spam. Full tab-by-tab tour → [**Features**](https://sikamikanikobg.github.io/homelab-monitor/features/). ## Multi-machine, in two sentences 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. Onboarding, Windows setup, and the security model → [**Multi-machine docs**](https://sikamikanikobg.github.io/homelab-monitor/multi-host/). ## Configuration 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**](https://sikamikanikobg.github.io/homelab-monitor/configuration/). ## Under the hood The hub stitches `nvidia-smi` (plus AMD GPUs via the in-kernel `amdgpu` sysfs interface, and AMD/Intel on Windows hosts via the built-in GPU perf counters), 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. - **30+ recognised model servers** → [Model servers](https://sikamikanikobg.github.io/homelab-monitor/model-servers/) - **Standard `/metrics` endpoint** to scrape into whatever dashboards you already run → [Metrics export](https://sikamikanikobg.github.io/homelab-monitor/prometheus/) - **The full data pipeline + caller attribution** → [How it works](https://sikamikanikobg.github.io/homelab-monitor/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](https://modelcontextprotocol.io) 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. ```bash # 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](https://sikamikanikobg.github.io/homelab-monitor/mcp/). ## 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](https://sikamikanikobg.github.io/homelab-monitor/how-it-works/). ## ⭐ 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! ## 💬 Community Building this is more fun together. **[Join the HomeLab Monitor Discord](https://discord.gg/tpKWKEdSQN)** — say hi, show off your rig, swap ideas, ask for help, or just hang out. It's where the roadmap chatter, “should we build X?” questions, and quick help happen — and where new contributors get a warm welcome. [](https://discord.gg/tpKWKEdSQN) Bring a friend, post an idea, open an issue — let's grow a friendly, healthy homelab community. 💛 ## 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](CONTRIBUTING.md). ## License MIT — see [LICENSE](LICENSE). ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [SikamikanikoBG](https://github.com/SikamikanikoBG) - **Source:** [SikamikanikoBG/homelab-monitor](https://github.com/SikamikanikoBG/homelab-monitor) - **License:** MIT - **Homepage:** https://sikamikanikobg.github.io/homelab-monitor/ Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Versions - **0.14.2** — security scan: pending review — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-sikamikanikobg-homelab-monitor - Seller: https://agentstack.voostack.com/s/sikamikanikobg - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.