You can already ask an LLM for advice on how to dial your espresso, and models like Claude, ChatGPT, or Gemini can already generate Gaggimate
If you already use Gaggimate MCP, the gaggimate mcp mcp server is the piece that lets your assistant work with it directly. You can already ask an LLM for advice on how to dial your espresso, and models like Claude, ChatGPT, or Gemini can already generate Gaggimate profiles in JSON format.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
manage_profile — Create, view, update, delete, and list brewing profiles on your Gaggimate device. Profiles define the entire extraction process—water temperatureanalyze_shot — Retrieve and analyze any shot with a 3-level detail system that balances insight vs. token cost:manage_shot_notes — Record ratings (0-5 stars), tasting notes, and brewing parameters for any shot. Notes are synced directly to your Gaggimate device via WebSocket andlist_recent_shots — Browse your shot history with optional filtering. Returns a list of recent shots with their IDs, timestamps, profile names, and any ratings you'vediagnose_connection — Troubleshoot connectivity issues between the MCP server and your Gaggimate device. Runs automated tests for network reachability, HTTP port accessmanage_coffee — Create and manage coffee tracking files. Each coffee gets a markdown file with bean profile, brewing approach (narrative), and a brewing journal withmanage_user_setup — Store and retrieve your equipment setup and preferences — machine, grinder, basket, scale, drink preferences, puck prep routine. Saved locally andmanage_grind_map — Track successful grind settings across different coffees. When you find a setting that works (4-5 star shots), record it here for future referencemanage_brewing_insights — Accumulate cross-coffee patterns and learnings. When the agent notices patterns (e.g., "Brazilian naturals do well with declining profiles"), itPlenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Gaggimate MCP's toolset — manage_profile, analyze_shot, manage_shot_notes and 6 more — is a fair guide to whether it matches your workflow. It is maintained by julianleopold; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| manage_profile | Create, view, update, delete, and list brewing profiles on your Gaggimate device. Profiles define the entire extraction process—water temperature, pre-infusion settings, pressure curves, and flow targets. The AI can buil |
| analyze_shot | Retrieve and analyze any shot with a **3-level detail system** that balances insight vs. token cost: |
| manage_shot_notes | Record ratings (0-5 stars), tasting notes, and brewing parameters for any shot. Notes are synced directly to your Gaggimate device via WebSocket and also stored locally as backup. You can track taste balance (bitter/bala |
| list_recent_shots | Browse your shot history with optional filtering. Returns a list of recent shots with their IDs, timestamps, profile names, and any ratings you've recorded. This helps the AI understand your brewing patterns and find sho |
| diagnose_connection | Troubleshoot connectivity issues between the MCP server and your Gaggimate device. Runs automated tests for network reachability, HTTP port access, API availability, and common misconfigurations. Returns specific recomme |
| manage_coffee | Create and manage coffee tracking files. Each coffee gets a markdown file with bean profile, brewing approach (narrative), and a brewing journal with dated analysis entries. The agent records what worked, what didn't, an |
| manage_user_setup | Store and retrieve your equipment setup and preferences — machine, grinder, basket, scale, drink preferences, puck prep routine. Saved locally and accessible across all sessions. |
| manage_grind_map | Track successful grind settings across different coffees. When you find a setting that works (4-5 star shots), record it here for future reference when you revisit a coffee or try something similar. |
| manage_brewing_insights | Accumulate cross-coffee patterns and learnings. When the agent notices patterns (e.g., "Brazilian naturals do well with declining profiles"), it records them here. The new-coffee skill reviews this file first when dialin |
{
"mcpServers": {
"gaggimate": {
"command": "/opt/homebrew/bin/uv",
"args": [
"--directory",
"/Users/yourname/code/gaggimate-mcp",
"run",
"mcp",
"run",
"src/gaggimate_mcp/server.py"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.