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Starburst MCP Server

Provider-hosted MCP server that lets AI agents run read-only Trino SQL queries across data mesh sources connected to Starburst Galaxy or Enterprise.

Analytics by Starburst Data OAuth2, Basic Auth active
Overview

Starburst's MCP server exposes the Starburst analytics platform (built on Trino) to AI agents and LLM applications. It is available in two flavors: a fully managed, multi-tenant service hosted inside Starburst Galaxy's control plane, and an integrated MCP server that ships with Starburst Enterprise for self-hosted clusters. In both cases, agents connect over HTTP and submit SQL strings, receiving structured JSON results with column metadata and rows.

The server is intentionally read-only. SQL parsing rejects any data-modifying statements (INSERT, UPDATE, DELETE, MERGE, TRUNCATE, CREATE, ALTER, DROP, GRANT, REVOKE) before queries reach the engine, leaving only SELECT, SHOW, and non-destructive EXPLAIN operations. All requests inherit Starburst's existing role-based and attribute-based access controls through Starburst Gravity, and every query is audit-logged with origin metadata (source tag, client user-agent, trace token).

What makes it notable is the federated reach: a single MCP endpoint lets an agent query across any source Trino connects to (S3/Iceberg, Snowflake, Postgres, MySQL, Kafka, Delta Lake, and more) without copying data. The Enterprise build adds higher-level tools beyond raw SQL, including data product search, parameterized query templates, and a stateful AI agent tool scoped to a data product.

Tools

Tool Description
queryReadOnly Execute a read-only SQL query (SELECT, SHOW, EXPLAIN) against the Starburst/Trino engine and return structured JSON with column metadata and rows.
searchDataProducts Keyword search across Starburst data product names, summaries, and descriptions to help agents discover governed datasets (Enterprise).
getDataProductDetails Retrieve full metadata for a specific data product, including views and materialized views (Enterprise).
listParametrizedQueryTools List pre-defined parameterized query templates exposed to agents (Enterprise).
parametrizedQuery Execute a pre-defined query template with validated parameters (Enterprise).
AI Agent tool Stateful conversational sessions scoped to a data product, backed by a configured language model (Enterprise).
Setup Guide

Prerequisites

  • A Starburst Galaxy account (Mission Critical, Enterprise, or free trial) OR a Starburst Enterprise cluster with a valid MCP license
  • An MCP-compatible client (Claude Desktop, Cursor, VS Code, etc.)
  • Credentials: either Galaxy username/password, a service account, or an OAuth client with the galaxy.mcp scope

Starburst Galaxy (hosted) endpoint

The hosted endpoint follows this pattern:

https://<account-name>.mcp.galaxy.starburst.io

Claude Desktop config (Basic Auth)

Base64-encode email:password and add it to your Claude Desktop config:

{
  "mcpServers": {
    "starburst-galaxy": {
      "type": "http",
      "url": "https://<account-name>.mcp.galaxy.starburst.io",
      "headers": {
        "Authorization": "Basic <base64_encoded_credentials>"
      }
    }
  }
}

Starburst Enterprise (self-hosted)

Enable the MCP server on the coordinator:

starburst.mcp.enabled=true

Then point clients at:

https://<coordinator-host>:<port>/mcp

Authentication uses standard Starburst auth (OAuth 2.0, delegated OAuth, or HTTP headers per the Client Protocol spec).

Notes and limits

  • Galaxy result sets are capped at 100KB; Enterprise defaults to 1MB (configurable 64KB to 8GB)
  • Enterprise query execution defaults to a 5 minute max
  • OAuth Dynamic Client Registration is not supported on Galaxy
  • Custom roles cannot be set for MCP operations; users see only resources their role permits
Use Cases
  • Let an analyst agent answer ad hoc business questions by writing SELECT queries across federated sources (S3/Iceberg + Snowflake + Postgres) without moving data
  • Have an agent discover the right governed dataset via searchDataProducts, inspect its schema with getDataProductDetails, then run a query against it
  • Expose curated, parameterized queries (e.g., "revenue by region for date X") that agents can invoke safely without writing raw SQL
  • Build a multi-agent analytics workflow where each agent has scoped, audit-logged access to specific data products under Starburst Gravity RBAC/ABAC
  • Use Claude or another LLM client to run SHOW CATALOGS, SHOW SCHEMAS, and EXPLAIN to explore a Trino-backed data mesh and document its structure
Example Prompts
  • "Show me total orders by region for the last 30 days using the sales catalog in Starburst."
  • "Search Starburst data products for anything related to customer churn and summarize the top three matches."
  • "List the catalogs and schemas in my Galaxy cluster, then describe the columns in the analytics.events table."
  • "Run the monthly_revenue_by_segment parameterized query for segment = 'enterprise' and month = '2026-04'."
  • "Explain the query plan for SELECT customer_id, SUM(amount) FROM iceberg.sales.orders GROUP BY 1 and tell me which join strategy Trino will use."
Pros
  • Official, vendor-hosted MCP service for Galaxy customers, with no local binary to install or maintain
  • Federated reach across every source Trino can connect to, queried in place without data copies
  • Enforced read-only semantics plus full RBAC/ABAC, audit logging, and trace tagging through Starburst Gravity
  • Enterprise build adds higher-level tools (data product search, parameterized queries, AI agent sessions) beyond raw SQL
Limitations
  • No write operations of any kind: INSERT, UPDATE, DELETE, and DDL are all rejected
  • Galaxy result sets capped at 100KB, which can truncate large analytical responses
  • Galaxy MCP requires a paid Mission Critical/Enterprise plan (or trial); Enterprise MCP requires a licensed cluster and explicit enablement
  • OAuth Dynamic Client Registration and custom MCP-scoped roles are not supported on Galaxy
Alternatives
  • Trino MCP server (community implementations) for self-hosted open-source Trino clusters
  • Snowflake's official MCP/Cortex integrations for Snowflake-native analytics
  • Databricks Genie / SQL MCP integrations for Lakehouse-based federated SQL