Trino MCP Server

Overview

The Trino MCP Server bridges an AI assistant with a high‑performance, distributed SQL engine. By exposing Trino tables as MCP resources and offering a tool to run arbitrary SQL, it allows an AI model to query large datasets directly from the assistant’s context. This eliminates the need for manual data exports or separate ETL pipelines, giving developers a single, consistent interface to both structured and semi‑structured data.

The server’s core value lies in its ability to list tables from a specified catalog and schema, then present those tables as searchable resources. When an AI assistant receives a request to retrieve data, it can first browse the available tables, then use the server’s SQL execution tool to fetch rows or run analytics queries. This flow keeps data provenance explicit and lets the assistant transparently reference the exact table or query result in its response.

Key capabilities include:

• Resource enumeration: The server automatically discovers all tables in the configured catalog/schema and exposes them as MCP resources, enabling intuitive discovery for the assistant.
• Table reading: Clients can request a table’s contents, and the server streams the result set back in a structured format.
• SQL execution tool: Any valid SQL statement can be sent to Trino; the server returns the result set, allowing dynamic queries such as aggregations, joins, or sub‑queries.
• Environment‑driven configuration: Connection details are supplied via environment variables, keeping credentials out of code and simplifying deployment across environments.

Typical use cases involve data‑driven chatbots, analytics assistants, or automated reporting systems. For example, a customer support assistant can answer “How many orders were placed last month?” by querying the sales table directly, while a data scientist’s notebook can call the MCP server to fetch experimental results without writing boilerplate connection code.

Integration into AI workflows is straightforward: the MCP server registers its capabilities with an MCP‑aware agent, which then presents table names and SQL tools as part of the assistant’s toolset. When a user asks for data, the agent can automatically select the appropriate table resource or invoke the SQL tool, ensuring that the assistant’s responses are grounded in real, up‑to‑date data from Trino. This tight coupling enhances reliability and reduces cognitive load for developers building AI applications that depend on large‑scale analytics.