Gaggiuino MCP Server

A Remote MCP server for integrating a Gaggiuino espresso machine with AI tools. Ask your AI assistant to check machine status, analyze shot data, and get dial-in guidance.

Features

MCP Tools

Shot Analysis

• get_status - Current machine status (temperature, pressure, flow, weight)
• get_latest_shot_id - Most recent shot, id and headline numbers in one call
• list_recent_shots - The last few shots summarised, for trends over a session
• get_shot_data - Structured shot summary with metrics
• get_shot_raw_data - Complete time-series data
• view_shot_graph - Interactive shot graph rendered in MCP-compatible hosts (pressure, flow, weight over time with target overlays and optional shot comparison)

Profiles and Settings

• list_profiles - Profiles on the machine, merged with this server's documentation
• get_profile_info - Everything known about one profile
• get_machine_settings - Boiler, steam, and scale configuration as the machine reports it
• get_maintenance_status - Descale and backflush history the machine tracks itself, with shots since each
• get_dial_in_guidance - Expert guidance for analyzing espresso shots
• select_profile - Switch the active profile (changes the machine; requires an authenticated server)
• upload_profile - Save a new brew profile to the machine (changes the machine; requires an authenticated server). Creates only — it never updates, and the machine assigns a fresh id every time, so uploading twice leaves two profiles

MCP Prompts - workflow templates your host surfaces as slash commands or menu items:

• dial_in_new_bag - first shots on a coffee you have not pulled before (bean, and optionally roast level, dose, and what you want in the cup)
• diagnose_last_shot - read the shot you just pulled against how it tasted (what was wrong, and optionally what you changed)
• choose_profile - pick a profile the machine actually holds for a coffee (roast level, and optionally drink and notes)
• espresso_shot_analyst - the dial-in guidance as a system prompt (same content as get_dial_in_guidance)

Each workflow prompt lays out the tools to call in order, so the analysis starts from the machine's own data rather than a guess.

MCP Resources - gaggiuino://profiles and gaggiuino://profiles/{id} for profile data

Quick Start

The server is published as a multi-arch image (linux/amd64, linux/arm64) at ghcr.io/ljcl/gaggiuino-mcp, so there is nothing to clone or build. It is also listed in the MCP Registry as io.github.ljcl/gaggiuino-mcp.

1. Download and Configure

mkdir gaggiuino-mcp && cd gaggiuino-mcp

curl -O https://raw.githubusercontent.com/ljcl/gaggiuino-mcp/main/docker-compose.yml
curl -o .env https://raw.githubusercontent.com/ljcl/gaggiuino-mcp/main/.env.example

Edit .env with your Gaggiuino machine's address:

# Use the IP directly (recommended)
GAGGIUINO_URL=http://192.168.1.100

# Or if mDNS works on your network
GAGGIUINO_URL=http://gaggiuino.local

2. Start the Server

docker compose up -d

3. Verify

curl http://localhost:8000/health

The server is available at http://<your-docker-host>:8000/mcp.

Choosing a Version

The compose file tracks latest. To pin a release, set GAGGIUINO_MCP_TAG in .env:

GAGGIUINO_MCP_TAG=1.0    # latest 1.0.x patch
GAGGIUINO_MCP_TAG=1.0.1  # exact release

Upgrade with:

docker compose pull && docker compose up -d

Configuration

Environment Variables

Health and logs

GET /health returns JSON:

{
  "status": "ok",
  "version": "1.1.0",
  "uptimeSec": 3412,
  "machine": {
    "url": "http://gaggiuino.local",
    "state": "unreachable",
    "lastCheckedAt": "2026-07-27T21:11:15.274Z",
    "lastError": "Unable to connect. Is the computer able to access the url?",
    "versions": {
      "coreVersion": "a06f97fd",
      "frontVersion": "a06f97fd",
      "staticVersion": "a06f97fd"
    }
  }
}

It answers 200 whenever the process is alive, including while the machine is unreachable — your espresso machine is off most of the day, and the container healthcheck reads the status code. machine.state is ok, unreachable, or unknown, observed from the requests the server already makes rather than from a probe, so /health puts no extra load on the machine. machine.versions is observed the same way — remembered the first time anything reads the machine's settings (get_machine_settings), never fetched by /health itself — so null there means "this server has not read the settings yet", not "the machine reports no version". Only those three documented fields are published: /health is unauthenticated, so anything a future firmware adds under versions stays out of it until someone decides otherwise.

Logs are one JSON object per line, so you can pick out what you need:

docker compose logs -f | jq -c 'select(.event == "tool.call" and .outcome != "ok")'

Securing the endpoint

Turn on OAuth before exposing this server beyond your LAN. Every tunnel option below puts /mcp on the public internet, and without authentication anyone who learns the URL gets the full tool surface against a machine in your kitchen. The server prints a warning at startup while nothing is configured.

Three variables, all required together:

# 1. The public origin clients will reach the server on — no path, https only.
#    This is advertised as the OAuth `resource`, so it must match the URL you
#    type into Claude exactly.
MCP_PUBLIC_URL=https://your-machine.tail-scale.ts.net

# 2. The key your tokens are signed with. Keep it stable across restarts, or
#    every restart signs you out of your phone.
openssl rand -hex 32

# 3. The passphrase you will type on the consent page. Store the hash, never
#    the passphrase — this prompts and prints the whole line to paste in.
cd apps/server && bun run hash-passphrase >> ../../.env

Then recreate the container — a plain restart is not enough:

docker compose up -d --force-recreate

Compose tracks the list of env_file paths, not their contents, so editing .env and running docker compose up -d (or restart) can reuse the existing container along with the environment it was created with. The new variables never reach the process and the server comes up unauthenticated exactly as if you had not set them — with no error, because from its point of view nothing is configured. Check what actually arrived:

docker inspect gaggiuino-mcp --format '{{range .Config.Env}}{{println .}}{{end}}' | grep MCP_

Setting only some of them fails at startup and names the missing one. That is deliberate: silently falling back to an open endpoint is how somebody exposes a tunnel believing it is protected. The corollary is worth knowing when diagnosing: a server that is running and unauthenticated has seen none of the three — if you believe you set them, the container is stale, not the config wrong.

Why OAuth and not a shared token

There used to be a MCP_AUTH_TOKEN shared secret. It was removed in 2.0.0, because a Claude connector could never present it. A connector is added at the account level so one entry has to work on claude.ai, Claude Desktop and iOS, and on a personal plan the "Add custom connector" dialog offers an OAuth Client ID and Secret and no request-header field. A local stdio bridge is not a way around it either — it cannot run on iOS. So on the deployment this project is built for, the token could never leave the client, and the two write tools stayed permanently refused.

If you set that variable, unset it. The server refuses to start while it has a value, and says so by name. That is on purpose: an ignored variable would have meant a deployment that used the token to gate /mcp coming up wide open on the next image pull, with nothing in the log to say the gate had gone. The check is removed a release from now.

When you connect, Claude discovers the endpoint, sends you to a consent page served by this server, and you type the passphrase. There is nothing to register and no client secret to store.

What each part protects

/health and the /.well-known/* discovery documents are deliberately unauthenticated — the container's healthcheck presents no credential, and a document a client fetches in order to authenticate cannot itself require authentication.

select_profile and upload_profile — the two tools that change the machine — need the espresso:write scope. A token without it gets a 403 that prompts Claude to ask you for the extra permission rather than failing silently. With nothing configured at all they refuse to run and say so, which is why an open server is a defensible default for a LAN and a machine-control tool on one is not.

Requests carrying an Origin header are rejected unless the origin is listed in MCP_ALLOWED_ORIGINS. This is what stops any web page you happen to visit from POSTing to a server running on your own network — a token does not help there, because the browser sends it for you. Requests with no Origin (Claude Desktop, curl, anything that is not a browser) are unaffected, so the default empty list is the right setting for almost everyone.

Listing an origin also makes /mcp answer that origin's CORS preflight and echo Access-Control-Allow-Origin (plus Access-Control-Expose-Headers: mcp-session-id, without which a browser client can read the handshake but not the session it needs to continue with). Allowing an origin the browser then blocks would be an allowlist that allows nothing.

scripts/test-auth.sh probes a running server for all of the above — the discovery chain, the shape of the 401, cross-host redirects and origin validation. Run it from outside your LAN. Every failure it catches is a failure of the URL as Claude reaches it, and the one that bites most often — MCP_PUBLIC_URL disagreeing with the URL you typed into Claude — is invisible from localhost.

BASE_URL=https://your-machine.tail-scale.ts.net ./scripts/test-auth.sh

Customization

The server ships with generic profiles and prompts. Two local override files are merged on top of the defaults at startup:

1. prompts.local.yaml - equipment-specific dial-in guidance (your grinder model, basket, and other equipment details).

2. profiles.local.yaml - your own profiles, or overrides/removals of the defaults (set a profile ID to null to remove it).

Start from the examples:

curl -O https://raw.githubusercontent.com/ljcl/gaggiuino-mcp/main/apps/server/src/data/prompts.example-local.yaml
curl -O https://raw.githubusercontent.com/ljcl/gaggiuino-mcp/main/apps/server/src/data/profiles.example-local.yaml

These hold personal equipment configuration, so they are deliberately never baked into the published image. To use them, uncomment the volumes: block in docker-compose.yml:

volumes:
  - ./profiles.local.yaml:/app/apps/server/src/data/profiles.local.yaml:ro
  - ./prompts.local.yaml:/app/apps/server/src/data/prompts.local.yaml:ro

From a repo checkout, copy each *.example-local.yaml to *.local.yaml alongside it in apps/server/src/data/ instead - they are gitignored and picked up automatically.

Connecting to AI Tools

Many AI tools (like Claude Desktop) route MCP requests through their own servers, not from your local machine. This means your MCP server needs to be accessible via a public HTTPS URL.

Every option in this section publishes /mcp to the internet. Turn on OAuth first — see Securing the endpoint — and set MCP_PUBLIC_URL to the exact origin you are about to publish.

Whichever ingress you pick, three things decide whether it works:

• One origin serves everything. /mcp, /.well-known/* and /oauth/* all have to be reachable at MCP_PUBLIC_URL. That is what keeps this one container.
• Use a stable hostname. A quick-tunnel hostname that rotates on restart changes the advertised resource, and the connector breaks every time.
• No cross-host redirects. If the registered URL 301/302/307/308s to a different host, the Authorization header is dropped on the way. This is the usual cause of "works in MCP Inspector or Claude Code but not claude.ai" — apex-to-www canonicalisation in front of the server is the common way to hit it. scripts/test-auth.sh checks for it.

Claude caches discovery documents globally by URL for about five minutes, so a metadata change is not live immediately — and a broken deploy's metadata can be served for a few minutes after you fix it.

Tailscale Funnel (Recommended)

Tailscale Funnel exposes your server to the internet via a secure HTTPS URL:

tailscale funnel --bg 8000
# URL: https://your-machine.tail-scale.ts.net/mcp

# then, in your .env
MCP_PUBLIC_URL=https://your-machine.tail-scale.ts.net

Funnel, not Serve. Claude reaches your connector from Anthropic's own infrastructure, and it refuses a hostname before sending a byte if any resolved address is not globally routable — explicitly including 100.64.0.0/10, which is the tailnet's own range. tailscale serve publishes exactly those addresses. Funnel publishes public records pointing at Tailscale's relays instead. Connectors are also IPv4-only, so a hostname publishing only AAAA records cannot be reached. One check covers both:

dig +short your-machine.tail-scale.ts.net   # must return a routable IPv4 address

Two things people reach for here and should not:

• Do not allowlist Anthropic's egress range (160.79.104.0/21) as an access control. Funnel does not forward the client IP, and /oauth/authorize is reached by your own browser, not by Anthropic — so an IP allowlist breaks the login while protecting nothing.
• Do not trust the Tailscale-User-Login header. tailscaled does strip forged copies, but only on traffic it proxies. This project ships network_mode: host with HOST=0.0.0.0, so the listener is directly reachable and anything on the host can set that header itself. Believing it would be a write-scoped authentication bypass.

Cloudflare Tunnel

Use cloudflared to create a persistent tunnel to your server. Use a named tunnel with a hostname you own, not a quick tunnel — a trycloudflare.com hostname changes on every restart, and MCP_PUBLIC_URL has to change with it.

ngrok

ngrok http 8000

Same caveat: a free ngrok hostname rotates. Reserve a domain, or expect to re-add the connector each time.

Behind your own reverse proxy

Proxy all three paths — /mcp, /.well-known/* and /oauth/* — to the same upstream, and set MCP_PUBLIC_URL to the public origin. The proxy must not canonicalise across hosts (see the redirect note above), and it must pass the Authorization header through untouched.

Using an external identity provider

Not yet supported — the built-in authorization server is the only mode. If you already run Authentik, Authelia, Keycloak, Zitadel, Kanidm or tsidp and would rather point at it, that is tracked in #110.

Local Network Only

If your AI tool connects directly (e.g. local MCP server config), use the direct address:

http://<docker-host-ip>:8000/mcp

Adding to Claude Desktop

1. Go to Settings > Integrations > Add More > Add Remote MCP Server
2. Set the URL to your public HTTPS endpoint (e.g. https://your-machine.tail-scale.ts.net/mcp)
3. Save and enable

Architecture

AI Tool (Claude Desktop, etc.)
    |
    |  HTTPS
    v
+-----------------------------+
|  HTTPS Tunnel               |
|  (Tailscale / Cloudflare /  |
|   ngrok / reverse proxy)    |
+-----------------------------+
    |
    |  HTTP (localhost:8000)
    v
+-----------------------------+
|  Gaggiuino MCP Server       |
|  (Docker container)         |
|  Bun + Streamable HTTP      |
+-----------------------------+
    |
    |  HTTP (local network)
    v
+-----------------------------+
|  Gaggiuino                  |
+-----------------------------+

Development

git clone https://github.com/ljcl/gaggiuino-mcp.git
cd gaggiuino-mcp

bun install          # Install all dependencies

bun run build        # Build all packages (Turborepo)
bun run test         # Run all tests
bun run lint         # Lint all packages
bun run check        # lint + test + typecheck + build + knip + boundaries

# Server
cd apps/server
bun run dev          # Watch mode
bun run test         # Server tests only

# Shot graph UI (run from the repo root)
bun run storybook    # Storybook on port 6006

# Regenerate JSON schemas (after changing Zod schemas in loader.ts)
cd apps/server
bun run generate-schemas

The main branch Storybook is published to GitHub Pages at ljcl.github.io/gaggiuino-mcp — a static build for browsing the shot-graph and UI components without running anything locally.

Docker

docker-compose.yml pulls the published image. To build and run the image from your checkout instead, layer the build override on top of it:

docker compose -f docker-compose.yml -f docker-compose.build.yml build
docker compose -f docker-compose.yml -f docker-compose.build.yml up -d
docker compose logs -f

The override tags the result ghcr.io/ljcl/gaggiuino-mcp:dev so a local build is never mistaken for a published release.

Troubleshooting

Can't connect to gaggiuino.local

mDNS (.local hostnames) may not work inside Docker containers. Use the IP address directly in GAGGIUINO_URL.

AI tool can't reach the server

Ensure your server is publicly accessible via HTTPS. Tools like Claude Desktop route requests through their own servers, so Tailnet-only access (e.g. tailscale serve) won't work - you need a public tunnel (e.g. tailscale funnel).

Server starts but can't reach Gaggiuino

The container uses network_mode: host to share the host's network stack. If your Gaggiuino is on a different network segment, adjust your Docker networking configuration.

License

MIT © Luke Clark