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Gaggiuino MCP Server

@AndrewKlement

リポジトリ

基本情報

カテゴリ

その他

ライセンス

MIT

トランスポート

stdio

作者

AndrewKlement

Gaggiuino MCP Server について

Gaggiuino MCP server

Gaggiuino MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "gaggiuino": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/PARENT/FOLDER/gaggiuino-mcp",
        "run",
        "gaggiuino.py"
      ]
    }
  }
}

Gaggiuino MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is gaggiuino-mcp?

gaggiuino-mcp is a lightweight Model Context Protocol (MCP) server built for Gaggiuino, the open-source espresso machine controller for the Gaggia Classic. It is designed to integrate easily with AI clients that want to display or analyze data from the Gaggiuino system in real time.

How to use gaggiuino-mcp?

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.

Key features of gaggiuino-mcp

  • 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

Use cases of gaggiuino-mcp

  • Analyzing latest espresso shot data with an AI assistant
  • Monitoring machine status (temperature, pressure) in real time
  • Automating shot logging and performance evaluation
  • Integrating espresso machine data into custom dashboards or workflows

よくある質問

1

What does gaggiuino-mcp do?

It reads real-time data from a Gaggiuino-controlled espresso machine — including current status, latest shot ID, and full shot data — and makes it available to AI clients via the Model Context Protocol.

2

What are the runtime dependencies?

The server runs as a Python script using `uv`. You need Python and `uv` installed, and the repository cloned locally.

3

Where does the data come from and where is it stored?

Data is fetched live from the Gaggiuino system on the local network. No external cloud storage is used; all data remains within the local network.

4

Are there any known limits or constraints?

The README does not mention specific limits. The server is designed for local network access only, and reliable operation depends on network connectivity to the Gaggiuino controller.

5

What transport and authentication does it use?

It uses standard MCP stdio transport (launched as a subprocess). No authentication is described; access is restricted by network boundaries (local network).

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