Gaggiuino MCP
About
An MCP server for the Gaggiuino open-source espresso machine, providing real-time local network access to machine status and shot data.
Details
- Author
- andrewklement
- GitHub stars
- 8
- Downloads
- 141
- Categories
- Other
- Tags
- #iot, #home-automation
Jump to
- Real-time access to shot telemetry
- Retrieve current espresso machine status
- Access the latest espresso shot ID
- Fetch detailed shot data for a given ID
- Designed for local network access
- Lightweight and easy to set up
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Gaggiuino MCPCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Configure the server in your MCP client (e.g., Claude Desktop) using the uv command pointing to the gaggiuino.py script in the cloned repository. Alternatively, install it automatically via Smithery with npx -y @smithery/cli install @AndrewKlement/gaggiuino-mcp --client claude. The server exposes three tools: getStatus, getLatestShotId, and getShotData.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"gaggiuino mcp": {
"gaggiuino": {
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/PARENT/FOLDER/gaggiuino-mcp",
"run",
"gaggiuino.py"
]
}
}
}
}
McpServers
{
"gaggiuino": {
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/PARENT/FOLDER/gaggiuino-mcp",
"run",
"gaggiuino.py"
]
}
}
Gaggiuino MCP Server This is a lightweight Model Context Protocol (MCP) server built forGaggiuino, the open-source espresso machine controller for the Gaggia Classic. It is designed to integrate easily AI clients that want to display or analyze data from the Gaggiuino system in real time.
This is an article I wrote about this projecthttps://medium.com/@a.klement.w/dialing-in-espresso-using-mcp-server-0207819fe93a
{ "mcpServers": { "gaggiuino": { "command": "uv", "args": [ "--directory", "/ABSOLUTE/PATH/TO/PARENT/FOLDER/gaggiuino-mcp", "run", "gaggiuino.py" ] } } }
To install Gaggiuino Espresso Machine Server for Claude Desktop automatically viaSmithery:
npx -y @smithery/cli install @AndrewKlement/gaggiuino-mcp --client claude
I'll retrieve and analyze the latest espresso shot data for you. Let me first check the status of your espresso machine and then get the latest shot information.getStatusNow I'll get the ID of the latest espresso shot:getLatestShotIdLet me retrieve the data for shot #188:getShotDataBased on the data retrieved, I'll now analyze your latest espresso shot (ID #188). Here's my analysis:
Machine Status and Shot Overview Your machine is currently idle at 57.2°C with -0.1 bar pressure. The shot analyzed was taken on April 19, 2025 (based on the timestamp), with a total duration of 26.5 seconds (265 datapoints at 0.1s intervals).
Profile Information You used the "Leva 9 LR v0.5" profile, which is a multi-phase profile with declining pressure targeting 40g of output. The profile has
- A flow-control preinfusion at 7 ml/s
- Step-down to 3.5 ml/s flow
- Pre-extraction at 1.5 ml/s flow with 3 bar pressure restriction
- Pressure ramp to 9 bar over 2 seconds
- A series of gradually declining pressure steps (8.8→6.0 bar)
Shot Analysis Preinfusion and Extraction Phases
- Preinfusion (0-5s): Started with a high flow rate (6-7 ml/s) with minimal pressure (0.3-0.6 bar), allowing the puck to saturate.
- First Pressure Rise (5-10s): Pressure gradually built to ~2.5 bar while flow decreased to ~1.5 ml/s.
- Pressure Profile Phase (10-15s): Pressure held around 3 bar with flow reduced to ~0.8 ml/s.
- Pressure Ramp (15-20s): Pressure increased from 3 to 8.9 bar over ~5 seconds following the programmed profile.
- Declining Pressure Phase (20-26.5s): Pressure gradually declined from ~8.5 to 6.4 bar following the profile steps.
- Final Yield: 38g (based on final shot weight)
- Brew Ratio: Unable to determine (coffee dose not provided)
- Temperature Stability: Excellent - maintained at 92.8-94.1°C throughout extraction
- First Drops: Appeared around 11-12 seconds (~75% into preinfusion)
- Peak Flow Rate: 7.2 ml/s (at the beginning)
- Peak Pressure: 8.9 bar (at ~20 seconds)
- Flow Rate: Started high (7 ml/s), stepped down to 3.5 ml/s then 1.5 ml/s during preinfusion as programmed, then stabilized at ~2.8 ml/s during extraction.
- Water Delivery: Total water pumped was 87.7 ml, with 38g in the cup, suggesting significant water retention in the puck.
- Pressure Profile: The pressure curve shows excellent progression from preinfusion through extraction, with a smooth transition to declining pressure.
Performance Assessment This appears to be a technically well-executed shot with good adherence to the programmed profile:
- Temperature Control: Excellent stability around 93-94°C
- Pressure Curve: Follows the programmed profile with smooth transitions
- Extraction Flow: Consistent during main extraction phase
- Weight Output: Achieved 38g (close to the 40g target)
Would you like me to provide more specific recommendations about any aspect of this shot, or is there a particular parameter you'd like me to analyze in more detail?
A free, private, and secure remote MCP server for Home Assistant.
MCP server for Google Cast — discover devices, play media, control volume, launch apps, and manage queues over stdio
A safe-by-default MCP server that exposes your Control4 home automation (lights, scenes, locks, thermostats, and media) as structured tools over HTTP and Claude Desktop STDIO for reliable AI-powered control on your local network.
Control and query the status of Ecovacs cleaning robots using the MCP protocol.
Control AVM FRITZ!Box routers - manage devices, WiFi, network settings, parental controls, and schedule time-delayed actions
Allows an LLM agent to control your Gaggimate espresso machine
A Model Context Protocol (MCP) server that provides AI assistants with access to Home Assistant, enabling smart home control and automation management.
Read-only MCP (Model Context Protocol) server for Home Assistant. Gives AI assistants (Claude Desktop, LibreChat, Cline) full observability into your smart home — entity states, automations, scripts, devices, logs, diagnostics — without any write access. Also generates static AI context snapshots for RAG systems, ChatGPT Projects, Qwen, and other tools that accept custom knowledge files. Built in Python, runs anywhere — locally, in Docker, or as an MCP integration.
Native macOS HomeKit integration for AI assistants via Model Context Protocol
Control a Keenetic router in plain language: which devices are on the network and what they are using, Wi-Fi and interface state, why the internet is down, routing policies, and isolated guest or IoT segments. Runs on your machine and talks to the router over the LAN through its own RCI API, with nothing installed on the router. Every change is read back and verified before it is reported as done, because the router accepts some wrong commands silently. Nothing is saved until you ask, and a backup is taken first.
Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.



