mcp-server-trino
Language:
Python
Stars:
10
Forks:
4
Trino MCP Server
This repository provides an MCP (Model-Control-Protocol) server that allows you to list and query tables via Trino using Python.
Overview
- MCP: MCP is a protocol for bridging AI models, data, and tools. This example MCP server provides:
- A list of Trino tables as MCP resources
- Ability to read table contents through MCP
- A tool for executing arbitrary SQL queries against Trino
- Trino: A fast, distributed SQL query engine for big data analytics. This server makes use of Trino’s Python client (trino.dbapi) to connect to a Trino host, catalog, and schema.
Requirements
- Python 3.9+ (or a version compatible with mcp, trino, and asyncio)
- trino (the Python driver for Trino)
- mcp (the Model-Control-Protocol Python library)
Configuration
The server reads Trino connection details from environment variables:
Variable | Description | Default |
---|---|---|
TRINO_HOST | Trino server hostname or IP | localhost |
TRINO_PORT | Trino server port | 8080 |
TRINO_USER | Trino user name | required |
TRINO_PASSWORD | Trino password (optional, depends on your authentication setup) | (empty) |
TRINO_CATALOG | Default catalog to use (e.g., hive , tpch , postgresql , etc.) | required |
TRINO_SCHEMA | Default schema to use (e.g., default , public , etc.) | required |
Usage
{
"mcpServers": {
"trino": {
"command": "uv",
"args": [
"--directory",
"",
"run",
"mcp_server_trino"
],
"env": {
"TRINO_HOST": "",
"TRINO_PORT": "",
"TRINO_USER": "",
"TRINO_PASSWORD": "",
"TRINO_CATALOG": "",
"TRINO_SCHEMA": ""
}
}
}
}
Publisher info
More MCP servers built with Python
Create, backtest, and execute trades directly in one chat box. The Composer MCP Server gives LLMs the power to backtest investment ideas and execute automated trading strategies. Trade across stocks, ETFs, and crypto directly in Claude.
An MCP to generate presentations with AI. Create and edit PowerPoint presentations with AI.
The PaddleOCR MCP server brings enterprise-grade OCR and document parsing capabilities to AI applications. Built on PaddleOCR — a proven solution with 50,000+ GitHub stars, deeply integrated by leading projects like MinerU, RAGFlow, and OmniParser— with targeted optimizations based on the MCP concept.