Turn Your AI Assistant into a Trino Data Analyst

Your AI assistant can now query your Trino clusters directly. No more copying SQL back and forth, no more manual schema exploration – just ask questions in plain English and get answers from your data warehouse.

Why This Changes Everything

If you're running Trino for analytics, you know the drill: someone asks "What were our top customers last quarter?" and you end up writing SQL, running it, formatting results, then explaining what you found. Now your AI assistant handles the entire workflow.

Instead of this manual process:

-- You manually write:
SHOW CATALOGS;
SHOW SCHEMAS FROM hive;
SHOW TABLES FROM hive.sales;
DESCRIBE hive.sales.customers;
SELECT region, COUNT(*) FROM hive.sales.customers 
GROUP BY region ORDER BY COUNT(*) DESC;

You just ask:

"How many customers do we have per region? Show them in descending order."

The AI discovers your schema, understands your data structure, writes the query, executes it, and explains the results – all in one conversation.

Built for Production Trino Environments

This isn't a prototype. It's a production-ready MCP server built in Go with:

• Enterprise Security: Read-only queries by default, SSL/TLS support, vulnerability scanning in CI
• Multiple Transport Options: STDIO for desktop AI apps, HTTP/SSE for web-based integrations
• Comprehensive Discovery: Catalogs, schemas, tables, and column metadata – your AI assistant maps your entire data landscape
• Battle-Tested: Proper error handling, query timeouts, connection pooling

What Your AI Assistant Can Do

Smart Schema Discovery

Your assistant explores your data warehouse intelligently:

AI: "Let me check what data you have available..."
→ Discovers catalogs: production, staging, analytics
→ Finds schemas: sales, marketing, finance
→ Maps tables: customers, orders, campaigns
→ Understands columns: names, types, relationships

Complex Analytics Made Simple

Ask business questions, get SQL answers:

"Show me our revenue trend by quarter"

-- AI generates and executes:
SELECT 
  YEAR(order_date) as year,
  QUARTER(order_date) as quarter,
  SUM(total_amount) as revenue
FROM sales.orders 
GROUP BY YEAR(order_date), QUARTER(order_date)
ORDER BY year, quarter;

"Which product categories are underperforming?"

-- AI writes:
WITH category_performance AS (
  SELECT 
    p.category,
    SUM(oi.quantity * oi.unit_price) as revenue,
    COUNT(DISTINCT o.order_id) as order_count
  FROM sales.orders o
  JOIN sales.order_items oi ON o.order_id = oi.order_id
  JOIN catalog.products p ON oi.product_id = p.product_id
  WHERE o.order_date >= DATE '2024-01-01'
  GROUP BY p.category
)
SELECT category, revenue, order_count
FROM category_performance
WHERE revenue < (SELECT AVG(revenue) FROM category_performance)
ORDER BY revenue ASC;

Installation That Actually Works

Homebrew (Recommended):

brew tap tuannvm/mcp
brew install mcp-trino

Docker (For Containerized Deployments):

docker run -e TRINO_HOST=your-trino-host ghcr.io/tuannvm/mcp-trino:latest

Binary Download: Grab the appropriate binary for your platform from GitHub Releases.

Connect to Your AI Client

Cursor Integration

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "trino": {
      "command": "mcp-trino",
      "env": {
        "TRINO_HOST": "your-cluster.company.com",
        "TRINO_PORT": "8443",
        "TRINO_USER": "your-username"
      }
    }
  }
}

Claude Desktop

Update your Claude config:

{
  "mcpServers": {
    "trino": {
      "command": "mcp-trino",
      "env": {
        "TRINO_HOST": "your-cluster.company.com",
        "TRINO_USER": "your-username",
        "TRINO_PASSWORD": "your-password"
      }
    }
  }
}

Works with Windsurf, ChatWise, and any MCP-compatible client.

Real-World Workflow

Here's how a typical data analysis session looks:

You: "I need to analyze customer churn. What data do we have?"

AI Assistant:

1. Discovers your catalogs and schemas
2. Finds customer and transaction tables
3. Examines the schema structure
4. Suggests: "I found customer data in analytics.customers with signup dates, and transaction data in sales.orders. I can analyze churn by looking at customers who haven't made purchases recently."

You: "Perfect. Show me customers who haven't ordered in the last 90 days."

AI Assistant:

SELECT 
  c.customer_id,
  c.email,
  c.signup_date,
  MAX(o.order_date) as last_order_date,
  DATE_DIFF('day', MAX(o.order_date), CURRENT_DATE) as days_since_last_order
FROM analytics.customers c
LEFT JOIN sales.orders o ON c.customer_id = o.customer_id
GROUP BY c.customer_id, c.email, c.signup_date
HAVING MAX(o.order_date) < CURRENT_DATE - INTERVAL '90' DAY
   OR MAX(o.order_date) IS NULL
ORDER BY days_since_last_order DESC NULLS LAST;

Returns 1,247 at-risk customers with their details.

You: "Now segment them by signup cohort."

AI Assistant: Automatically builds on the previous query, adding cohort analysis...

Security That Doesn't Get in the Way

• Read-only by default: Prevents accidental data modification
• Configurable permissions: Enable writes only when needed
• Connection security: Full SSL/TLS support for encrypted connections
• Environment-based config: Keep credentials out of configuration files

Why Go?

This server is built in Go because your data infrastructure deserves better than Node.js for high-performance SQL execution:

• Concurrent query handling without event loop blocking
• Memory efficiency for large result sets
• Fast startup times for containerized deployments
• Cross-platform binaries with zero runtime dependencies

Get Started in 2 Minutes

1. Install: brew install tuannvm/mcp/mcp-trino
2. Configure: Set your Trino connection details
3. Connect: Add to your AI client configuration
4. Ask: "What data do we have?" and watch your assistant explore

Your AI assistant is about to become your most productive data analyst. No SQL knowledge required on your end – just ask questions and get insights.

Links:

• GitHub Repository
• Documentation
• Latest Release