Bridging AI and Big Data: An MCP Server for Trino

This document details the implementation and usage of an MCP (Model Context Protocol) server designed to seamlessly integrate Trino, a high-performance distributed SQL query engine, with AI models and applications. This server empowers AI agents to discover, query, and leverage data residing within Trino, unlocking powerful new capabilities for data-driven AI workflows.

Core Concepts

• Model Context Protocol (MCP): MCP acts as a standardized bridge between AI models, external data sources, and tools. This server leverages MCP to expose Trino's capabilities to AI agents in a structured and discoverable manner.
• Trino: Trino is a distributed SQL query engine optimized for fast, interactive analytics over large datasets. This server utilizes the Trino Python client (trino.dbapi) to establish a connection and execute queries against a Trino cluster.

The MCP server provides the following key functionalities:

• Resource Discovery: Exposes Trino tables as MCP resources, allowing AI agents to discover available datasets.
• Data Access: Enables AI agents to read the contents of Trino tables through the MCP interface.
• Arbitrary Query Execution: Provides a tool for executing custom SQL queries against Trino, granting AI agents the flexibility to perform complex data analysis.

Prerequisites

• Python: Version 3.9 or higher is required, ensuring compatibility with the mcp, trino, and asyncio libraries.
• Trino Python Driver: Install the trino package, which provides the necessary functionality to connect to and interact with Trino.
• MCP Python Library: Install the mcp package, which provides the framework for building MCP-compliant servers.

Configuration

The server relies on environment variables for configuring the Trino connection. This approach promotes security and flexibility, allowing you to easily adapt the server to different Trino environments.

| Environment Variable | Description