grr-gaggiuino-mcp
An MCP server for Gaggiuino-modified espresso machines, enabling monitoring, shot analysis, and profile management.
README
grr-gaggiuino-mcp
An MCP (Model Context Protocol) server for Gaggiuino-modified espresso machines.
Monitor your machine, analyze shots, and manage brewing profiles from any MCP-compatible client.
Tools
| Tool | Description |
|---|---|
get_status |
Real-time machine state: temperature, pressure, weight, water level, active profile, brewing/steaming status |
get_shot |
Shot data with time-series curves (pressure, flow, temp, weight) and profile used. Defaults to latest shot. |
get_profiles |
List all brewing profiles with IDs and selection status |
select_profile |
Activate a brewing profile by ID |
Installation
Prerequisites
- Node.js 18+
- A Gaggiuino-modified espresso machine on your local network
Option 1: npx (easiest)
No install needed - just configure Claude Desktop to use npx:
{
"mcpServers": {
"gaggiuino": {
"command": "npx",
"args": ["grr-gaggiuino-mcp"],
"env": {
"GAGGIUINO_BASE_URL": "http://YOUR_GAGGIUINO_IP"
}
}
}
}
Option 2: Clone and Build
git clone https://github.com/sgerlach/grr-gaggiuino-mcp.git
cd grr-gaggiuino-mcp
npm install
npm run build
Claude Desktop Configuration
Add to your Claude Desktop config:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"gaggiuino": {
"command": "node",
"args": ["/path/to/grr-gaggiuino-mcp/dist/index.js"],
"env": {
"GAGGIUINO_BASE_URL": "http://YOUR_GAGGIUINO_IP"
}
}
}
}
Note: If using nvm, specify the full path to Node 18+:
"command": "/Users/you/.nvm/versions/node/v20.x.x/bin/node"
Configuration
| Variable | Default | Description |
|---|---|---|
GAGGIUINO_BASE_URL |
http://192.168.3.248 |
Your Gaggiuino's IP or hostname |
REQUEST_TIMEOUT |
5000 |
API timeout in milliseconds |
Testing
# With MCP Inspector
npm run inspect
# Direct run
GAGGIUINO_BASE_URL=http://YOUR_IP npm start
Example Workflows
Quick Status Check
You: "Is my machine ready to pull a shot?"
→ get_status: temp 93°C (target 93°C), pressure stable, water level 85%
Dialing In a New Coffee
You: "I have a new bag of coffee - Ethiopian Yirgacheffe, light roast,
tasting notes of blueberry and citrus. It's 10 days off roast.
What profile should I start with?"
→ LLM recommends a profile based on the coffee characteristics
You: "OK I pulled the shot, analyze it"
→ get_shot returns pressure/flow/temp curves
You: "It tasted sour and watery"
→ LLM analyzes the shot data against your taste feedback:
"The pressure ramped too quickly and flow was high (4.2 ml/s).
Try grinding 2 steps finer. The 6:1 ratio in 24s suggests
under-extraction - aim for 28-32s."
You: "Pulled another shot after adjusting"
→ get_shot for the new shot
→ "Much better - pressure curve is smoother, 2.8 ml/s flow.
How did it taste?"
Shot Analysis & Troubleshooting
You: "My shots have been channeling lately, look at my last few shots"
→ get_shot (latest), get_shot (id: previous)
→ LLM identifies pressure spikes/instability indicating channeling
You: "Compare this shot to yesterday's good one"
→ Side-by-side analysis of shot curves
Profile Management
You: "What profiles do I have?"
→ get_profiles lists all available profiles
You: "Switch to my low-pressure blooming profile"
→ select_profile activates it
You: "Pull a shot and tell me if this profile works for my current beans"
→ get_shot + analysis based on bean characteristics you've shared
Iterative Dial-In Session
You: "Let's dial in this coffee. Medium-dark Brazilian, chocolate/nutty,
for milk drinks. 18g dose, targeting 36g out."
→ LLM suggests starting parameters and profile
[Pull shot 1]
You: "Done, check it"
→ get_shot → "23 seconds, slightly fast. Bitter finish suggests
over-extraction at the end despite short time - likely channeling.
Grind 1 step finer and improve puck prep."
[Pull shot 2]
You: "Better prep this time"
→ get_shot → "27 seconds, smooth pressure curve. How's the taste?"
You: "Really good! Balanced, nice chocolate, good body for a cortado"
→ "Great - this is your baseline. Shot data saved.
Current recipe: 18g → 36g in 27s, Profile: X"
Unit Conversions
The Gaggiuino API returns values in deci-units. This server converts them to standard units:
| Raw API | Converted |
|---|---|
| deciseconds | seconds |
| decibar | bar |
| decidegrees | °C |
| decigrams | grams |
| deci-ml/s | ml/s |
API Reference
Based on the Gaggiuino REST API.
License
MIT
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。