Trino - AI Tool Review & Alternatives | Flaex AI

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Trino

Safety: 65/100

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MCP server for Trino, enabling AI assistants to interact with Trino's distributed SQL query engine via standardized tools and optional OAuth 2.1 authentication.

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Overview

MCP server for Trino, enabling AI assistants to interact with Trino's distributed SQL query engine via standardized tools and optional OAuth 2.1 authentication.

This MCP server provides a controlled interface to Trino. It is relatively safe when used with read-only queries and proper OAuth configuration. Enabling user impersonation and allowing arbitrary SQL execution increases the risk significantly.

Performance depends on the complexity of the SQL queries and the size of the data being processed. Restricting AI focus to specific schemas can significantly improve performance.

Cost is primarily related to Trino cluster resources consumed by query execution. Consider the cost of data transfer and compute resources.

HybridGo92 starsMITReleased 1/6/2026CI/CD

Connections & Capabilities

Connects To

GitHubPostgreSQLMySQLMongoDBDockerAzureS3

Capabilities

ReadWriteExec

Client Compatibility

Claude Desktop

full

Cursor

full

Windsurf

full

VS Code (Copilot)

untested

Quickstart

Install

brew install mcp-trino

Claude Desktop Config

{
  "mcpServers": {
    "mcp-trino": {
      "command": "docker",
      "args": ["run", "--rm", "-i",
               "-e", "TRINO_HOST=<HOST>",
               "-e", "TRINO_PORT=<PORT>",
               "-e", "TRINO_USER=<USERNAME>",
               "-e", "TRINO_PASSWORD=<PASSWORD>",
               "-e", "TRINO_SCHEME=http",
               "ghcr.io/tuannvm/mcp-trino:latest"],
      "env": {}
    }
  }
}

Environment Variables

MCP_TRANSPORT

Tools (6)

high

execute_query

Executes a SQL query against the Trino cluster.

Can modify or expose sensitive data depending on the query and user permissions.

safe

list_catalogs

Lists available catalogs in the Trino cluster.

Read-only operation that reveals catalog names.

safe

list_schemas

Lists available schemas within a specified catalog.

Read-only operation that reveals schema names.

safe

list_tables

Lists available tables within a specified schema.

Read-only operation that reveals table names.

safe

get_table_schema

Retrieves the schema of a specified table.

Read-only operation that reveals table structure.

safe

explain_query

Explains the execution plan of a SQL query.

Read-only operation that analyzes query execution.

Security

Auth Method

OAuth

Data Locality

hybrid

Known Risks

!SQL injection vulnerabilities in `execute_query` if input is not sanitized.
!Improperly configured OAuth can lead to unauthorized access.
!Exposure of Trino credentials if the server is compromised.
!Unintended data modification or deletion if write access is granted without proper controls.

Safety Score

moderate risk

Positive

+Optional OAuth 2.1 authentication with multiple providers (Okta, Google, Azure AD).
+Supports both native and proxy OAuth modes for varying client capabilities.
+Allows restricting AI focus to specific schemas for performance and security.
+Includes query attribution by tagging queries with OAuth user information.

Risks

-Executes SQL queries, potentially leading to data modification or information disclosure.
-User impersonation feature, if enabled, relies on Trino's permission system.
-Secrets management for proxy mode OAuth requires careful handling.
-Vulnerable to SQL injection if queries are not properly sanitized.

Use Cases

Data Exploration

-Discovering available datasets and their schemas.
-Previewing data through simple SELECT queries.
-Understanding query execution plans for optimization.

AI-Assisted Query Generation

-Generating SQL queries based on natural language input.
-Suggesting relevant tables and columns for analysis.
-Validating query syntax and semantics.

Data Governance

-Auditing query execution by attributing queries to users.
-Enforcing access control policies through user impersonation.
-Restricting AI access to specific schemas.

Who Is This For?

Best For

Organizations using Trino for data analytics.
Teams seeking to integrate AI assistants with their data workflows.
Scenarios requiring secure and auditable access to data.
Use cases where SQL is the primary query language.

Not Ideal For

Environments without a Trino cluster.
Use cases requiring real-time data access with low latency.
Organizations with strict data governance policies that prohibit AI access to data.

Autonomy & Execution

Default ModeConfigurable

Destructive Tools

Autonomy depends on the configured permissions and the specific SQL queries executed. User impersonation can grant the AI the same privileges as the impersonated user.

StatelessYes

Retry LogicNot documented

ConcurrencyNot documented

Production Tip

Monitor Trino query logs and resource usage to identify and address performance bottlenecks or security issues.

FAQ

What is the purpose of this MCP server?

It allows AI assistants to interact with a Trino cluster using standardized MCP tools.

What authentication methods are supported?

OAuth 2.1 is supported with providers like Okta, Google, and Azure AD.

How can I restrict AI access to specific data?

Use the `TRINO_ALLOWED_SCHEMAS` environment variable to limit the schemas the AI can access.

What is user impersonation and how does it work?

It allows the AI to execute queries as a specific user, inheriting their permissions. Enable it with `TRINO_ENABLE_IMPERSONATION=true`.

Is this server secure?

Security depends on proper configuration, especially OAuth and user impersonation settings. Follow security best practices.

What clients are compatible with this server?

Claude Desktop, Cursor, Windsurf, and any MCP-compatible client.

How do I monitor the server's performance?

Monitor Trino query logs and resource usage. The server tags queries with user identity for auditing.

Key Features

Model Context Protocol (MCP) support - Enables standardized communication between AI assistants and the Trino query engine.

High-performance Go implementation - Ensures efficient and low-latency interaction with distributed SQL environments.

OAuth 2.1 authentication - Provides secure, industry-standard identity management for AI-to-data access.

Distributed SQL integration - Allows AI tools to leverage Trino's capability to query massive datasets across multiple sources.

Use Cases & Problems Solved

Use Cases

•Use when you need to enable AI agents to execute complex analytical queries directly against large-scale distributed data lakes via Trino.
•Perfect for integrating secure, authenticated data exploration capabilities into custom LLM-powered data analysis applications.
•Ideal if you need to provide AI assistants with real-time access to heterogeneous data sources managed by a Trino cluster.
•Great for automating data retrieval tasks for automated reporting pipelines that require SQL-based data aggregation.
•Use when building internal data exploration chat interfaces that require the performance of a high-performance Go-based MCP implementation.

Problems Solved

✓Eliminates the need to build custom, non-standardized integrations for AI models to communicate with distributed SQL engines.
✓Reduces the complexity of managing authentication by supporting standardized OAuth 2.1 for secure AI-to-database interactions.
✓Removes integration latency by utilizing a high-performance, compiled Go-based server architecture for Model Context Protocol communication.
✓Solves the disconnect between LLM reasoning capabilities and live, large-scale enterprise data stored in distributed SQL environments.

Who It's For

Data engineers building AI-driven data analysis platformsLLM application developers integrating enterprise data sourcesBackend engineers focusing on Go-based infrastructure and MCP server deploymentsSolutions architects designing secure AI agent workflows for data-heavy environments

Metrics

Discovered2/14/2026

Reviews0

Saved By0 users

Trino

Overview

Connections & Capabilities

Connects To

Capabilities

Client Compatibility

Quickstart

Install

Claude Desktop Config

Environment Variables

Tools (6)

Security

Safety Score

Positive

Risks

Use Cases

Data Exploration

AI-Assisted Query Generation

Data Governance

Who Is This For?

Best For

Not Ideal For

Autonomy & Execution

FAQ

What is the purpose of this MCP server?

What authentication methods are supported?

How can I restrict AI access to specific data?

What is user impersonation and how does it work?

Is this server secure?

What clients are compatible with this server?

How do I monitor the server's performance?

Key Features

Use Cases & Problems Solved

Use Cases

Problems Solved

Who It's For

Metrics

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