MCP Trino Server

The MCP Trino Server bridges the Model Context Protocol (MCP) with Trino and Iceberg, giving AI assistants a standardized way to query large analytical datasets, manage table metadata, and perform routine maintenance tasks. By exposing Trino’s SQL engine as an MCP service, developers can let AI agents write and execute complex queries without leaving their conversational environment, while still benefiting from Trino’s distributed execution and Iceberg’s table format features.

What Problem Does It Solve?

Many data teams rely on Trino for fast, federated analytics across diverse sources. However, interacting with Trino traditionally requires command‑line tools or custom connectors in code, which is cumbersome for non‑technical users and limits the reach of AI assistants. The MCP Trino Server removes this barrier by presenting a simple, protocol‑driven interface that any AI client can consume. It allows assistants to request schema introspection, run ad‑hoc queries, and trigger maintenance operations—all through a consistent JSON payload—without the assistant needing to know Trino’s API details.

Core Capabilities

• SQL Execution: Accepts raw SQL statements, runs them on the configured Trino cluster, and returns results in a machine‑readable format that AI assistants can easily transform into natural language explanations or visualizations.
• Metadata Discovery: Provides catalog, schema, and table listings so assistants can guide users through available data assets or auto‑complete query templates.
• Iceberg Maintenance: Exposes commands for optimizing, repairing, and cleaning Iceberg tables, enabling AI agents to automate routine housekeeping tasks that keep analytical workloads performant.
• Result Formatting: Handles pagination, type mapping, and error reporting so the assistant can surface meaningful feedback or prompt for clarification when queries fail.
• Environment‑Aware Configuration: Reads connection details from environment variables, making it straightforward to deploy in Docker or local development environments.

Real‑World Use Cases

• Interactive Data Exploration: An AI assistant can walk a data analyst through the process of discovering tables, writing exploratory queries, and interpreting results—all within a chat interface.
• Automated Data Lake Hygiene: The assistant can schedule and trigger Iceberg maintenance jobs (e.g., or ) based on usage patterns, ensuring fast query performance without manual intervention.
• AI‑Powered BI Tools: Developers can build conversational dashboards where the assistant translates natural language questions into SQL, fetches results from Trino, and renders charts directly in the chat.
• On‑Demand Reporting: Executing ad‑hoc queries on demand for executives or stakeholders, with the assistant summarizing results and highlighting key metrics.

Integration Into AI Workflows

Once registered, any MCP‑compliant client—Claude Desktop, VS Code extensions, or custom applications—can request the server’s capabilities through simple JSON calls. The server’s responses are structured, enabling downstream processing such as natural‑language summarization, visualization generation, or further programmatic manipulation. Because the server abstracts Trino’s complexity, AI agents can focus on higher‑level reasoning and user interaction rather than low‑level query orchestration.

Unique Advantages

• Standardized Protocol: Leveraging MCP means the same client can talk to multiple data services (e.g., Snowflake, BigQuery) without changing the conversational logic.
• Python‑Based Extensibility: The server is written in Python, allowing developers to easily extend or customize behavior—such as adding custom authentication flows or integrating additional data‑format support.
• Docker Ready: With a ready‑made Docker Compose setup, teams can spin up a local Trino cluster and the MCP server in minutes, facilitating rapid prototyping.
• Iceberg Support: Native commands for Iceberg maintenance provide a level of data lake management that is rarely exposed via simple APIs, giving AI assistants deeper operational control.

In summary, the MCP Trino Server empowers developers to fuse conversational AI with high‑performance analytical queries and data lake maintenance, creating a seamless, protocol‑driven workflow that accelerates insight generation across enterprises.