What are the prerequisites to run MCP Trino Server?
You need Python 3.12 or higher and a running Trino server (Docker Compose is available for local development). Docker is optional but recommended for containerized deployment.
@alaturqua
基本情報
MCP Server for Trino developed via MCP Python SDK
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": {
"mcp-trino-python": {
"command": "npx",
"args": [
"-y",
"@smithery/cli",
"install",
"@alaturqua/mcp-trino-python",
"--client",
"claude"
]
}
}
}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。
上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。
MCP Trino Server is a Model Context Protocol (MCP) server that provides seamless integration with Trino and Iceberg, enabling advanced data exploration, querying, and table maintenance through a standard interface. It is designed for developers and data engineers building AI‑powered tools that interact with Trino databases.
Install via uv, pip, or Docker, then configure using environment variables (TRINO_HOST, TRINO_PORT, TRINO_USER, etc.). Run the server with python src/server.py (or the Docker equivalent) using one of three transport modes: stdio (default), streamable‑http, or sse. Connect an MCP client (e.g., VS Code or Claude Desktop) by providing the server’s command and configuration.
.env fileYou need Python 3.12 or higher and a running Trino server (Docker Compose is available for local development). Docker is optional but recommended for containerized deployment.
The server supports `stdio` (default, for local MCP clients like VS Code and Claude Desktop), `streamable‑http` (for remote or web access), and `sse` (Server‑Sent Events, legacy HTTP transport).
Configure via environment variables: `TRINO_HOST`, `TRINO_PORT`, `TRINO_USER`, `TRINO_CATALOG`, `TRINO_SCHEMA`, `TRINO_HTTP_SCHEME`, and optionally `TRINO_PASSWORD`. You can also use a `.env` file.
Yes, you can set the `TRINO_PASSWORD` environment variable to provide a password for authentication to the Trino server.
Yes. Build the Docker image with `docker build -t mcp-trino-python .` and run it with either `stdio` or `streamable‑http` transport. Docker Compose profiles are also provided for quick start.
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