"Trino" matching MCP servers:

A high-performance Model Context Protocol (MCP) server for Trino implemented in Go.

A Model Context Protocol server enabling AI assistants to query and explore data warehouses via Trino, with optional semantic context from metadata catalogs.

Enables executing SQL queries on Trino clusters via MCP, supporting multiple authentication methods and read/write operations with safety controls.

Enables querying Trino with SSO via OAuth2 external authentication, compatible with Cursor.

MCP server providing seamless integration with Trino and Iceberg for advanced data exploration, querying, and table maintenance.

Enables listing and querying Trino tables via MCP, supporting arbitrary SQL queries against a Trino cluster.

An MCP server that enables listing and querying Trino tables using Python, providing access to data resources through the Model-Control-Protocol.

Provides AI models with structured access to Trino's distributed SQL query engine, enabling LLMs to directly query and analyze data stored in Trino databases.

A Model Context Protocol server that provides seamless integration with Trino and Iceberg, enabling data exploration, querying, and table maintenance through a standard interface.

Enables database schema analysis and management for Trino servers through dynamic connections. Supports DDL validation, dependency analysis, schema documentation generation, and safe SQL execution with multiple concurrent connections.

Enables AI agents and MCP hosts to query Trino while carrying each end user's verified OIDC identity, so row filters, column masks, and catalog scope are enforced by the existing policy engine rather than a shared service account. Queries are read-only by construction, with quotas, revocation, and audit logging.

Model Context Protocol server for read-only access to Iceberg tables through Cloudera Trino: schema discovery, SQL queries, metadata-based health checks, time travel, and performance analysis.

Enables AI assistants to interact with multiple SQL databases (PostgreSQL, MySQL, SQL Server, SQLite, Trino) through MCP, reusing existing DBeaver connections for queries, schema management, and transactions, with SSH tunneling and read-only mode support.

Enables AI agents to query MySQL, ClickHouse, Trino, and S3-compatible object stores through the same local daemon used by VSCode, including running SQL from files and browsing buckets.

Enables natural-language queries on Trino big data platforms, generating validated, schema-aware SQL via RAG and local LLM inference, and exposes metadata, query, and profiling tools through MCP.

Enables AI-powered MCP clients to interact with data lakehouse components including Kafka, Flink, and Trino/Iceberg for managing topics, jobs, catalogs, and executing queries.

Enables AI agents to query Trino and Apache Pinot lakehouses under enforced governance, where every SQL statement is AST-validated, table-allowlisted, priced from the engine's own plan before it runs, and blocked or admitted against scan-byte and intermediate-row budgets. It also grounds agents with schema discovery tools, returns verified results with warnings instead of misleading answers, and records every tool call on an audit trail.