mcp-trino

A high-performance Model Context Protocol (MCP) server for Trino implemented in Go. This project enables AI assistants to seamlessly interact with Trino's distributed SQL query engine through standardized MCP tools.

This project implements a Model Context Protocol (MCP) server for Trino in Go. It enables AI assistants to access Trino's distributed SQL query engine through standardized MCP tools.

Trino (formerly PrestoSQL) is a powerful distributed SQL query engine designed for fast analytics on large datasets.

• ✅ MCP server implementation in Go
• ✅ Trino SQL query execution through MCP tools
• ✅ Catalog, schema, and table discovery
• ✅ Docker container support
• ✅ Supports both STDIO and HTTP transports
• ✅ Server-Sent Events (SSE) support for Cursor and other MCP clients
• ✅ Compatible with Cursor, Claude Desktop, Windsurf, ChatWise, and any MCP-compatible clients.

The easiest way to install mcp-trino is using Homebrew:

# Add the tap repository
brew tap tuannvm/mcp

# Install mcp-trino
brew install mcp-trino

To update to the latest version:

brew update && brew upgrade mcp-trino

1. Download the appropriate binary for your platform from the GitHub Releases page.
2. Place the binary in a directory included in your PATH (e.g., /usr/local/bin on Linux/macOS)
3. Make it executable (chmod +x mcp-trino on Linux/macOS)

git clone https://github.com/tuannvm/mcp-trino.git
cd mcp-trino
make build
# Binary will be in ./bin/

You can download pre-built binaries for your platform:

Or see all available downloads on the GitHub Releases page.

This MCP server can be integrated with several AI applications:

To use the Docker image instead of a local binary:

{
  "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": {}
    }
  }
}

Note: The host.docker.internal special DNS name allows the container to connect to services running on the host machine. If your Trino server is running elsewhere, replace with the appropriate host.

This Docker configuration can be used in any of the below applications.

To use with Cursor, create or edit ~/.cursor/mcp.json:

{
  "mcpServers": {
    "mcp-trino": {
      "command": "mcp-trino",
      "args": [],
      "env": {
        "TRINO_HOST": "<HOST>",
        "TRINO_PORT": "<PORT>",
        "TRINO_USER": "<USERNAME>",
        "TRINO_PASSWORD": "<PASSWORD>"
      }
    }
  }
}

Replace the environment variables with your specific Trino configuration.

For HTTP+SSE transport mode (supported for Cursor integration):

{
  "mcpServers": {
    "mcp-trino-http": {
      "url": "http://localhost:9097/sse"
    }
  }
}

Then start the server in a separate terminal with:

MCP_TRANSPORT=http TRINO_HOST=<HOST> TRINO_PORT=<PORT> TRINO_USER=<USERNAME> TRINO_PASSWORD=<PASSWORD> mcp-trino

To use with Claude Desktop, edit your Claude configuration file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "mcp-trino": {
      "command": "mcp-trino",
      "args": [],
      "env": {
        "TRINO_HOST": "<HOST>",
        "TRINO_PORT": "<PORT>",
        "TRINO_USER": "<USERNAME>",
        "TRINO_PASSWORD": "<PASSWORD>"
      }
    }
  }
}

After updating the configuration, restart Claude Desktop. You should see the MCP tools available in the tools menu.

To use with Windsurf, create or edit your mcp_config.json:

{
  "mcpServers": {
    "mcp-trino": {
      "command": "mcp-trino",
      "args": [],
      "env": {
        "TRINO_HOST": "<HOST>",
        "TRINO_PORT": "<PORT>",
        "TRINO_USER": "<USERNAME>",
        "TRINO_PASSWORD": "<PASSWORD>"
      }
    }
  }
}

Restart Windsurf to apply the changes. The Trino MCP tools will be available to the Cascade AI.

To use with ChatWise, follow these steps:

1. Open ChatWise and go to Settings
2. Navigate to the Tools section
3. Click the "+" icon to add a new tool
4. Select "Command Line MCP"
5. Configure with the following details:
   • ID: mcp-trino (or any name you prefer)
   • Command: mcp-trino
   • Args: (leave empty)
   • Env: Add the following environment variables:

     TRINO_HOST=<HOST>
     TRINO_PORT=<PORT>
     TRINO_USER=<USERNAME>
     TRINO_PASSWORD=<PASSWORD>
     

Alternatively, you can import the configuration from JSON:

1. Copy this JSON to your clipboard:

   {
     "mcpServers": {
       "mcp-trino": {
         "command": "mcp-trino",
         "args": [],
         "env": {
           "TRINO_HOST": "<HOST>",
           "TRINO_PORT": "<PORT>",
           "TRINO_USER": "<USERNAME>",
           "TRINO_PASSWORD": "<PASSWORD>"
         }
       }
     }
   }

2. In ChatWise Settings > Tools, click the "+" icon
3. Select "Import JSON from Clipboard"
4. Toggle the switch next to the tool to enable it

Once enabled, click the hammer icon below the input box in ChatWise to access Trino MCP tools.

The server provides the following MCP tools:

Execute a SQL query against Trino with full SQL support for complex analytical queries.

Sample Prompt:

"How many customers do we have per region? Can you show them in descending order?"

Example:

{
  "query": "SELECT region, COUNT(*) as customer_count FROM tpch.tiny.customer GROUP BY region ORDER BY customer_count DESC"
}

Response:

{
  "columns": ["region", "customer_count"],
  "data": [
    ["AFRICA", 5],
    ["AMERICA", 5],
    ["ASIA", 5],
    ["EUROPE", 5],
    ["MIDDLE EAST", 5]
  ]
}

List all catalogs available in the Trino server, providing a comprehensive view of your data ecosystem.

Sample Prompt:

"What databases do we have access to in our Trino environment?"

Example:

{}

Response:

{
  "catalogs": ["tpch", "memory", "system", "jmx"]
}

List all schemas in a catalog, helping you navigate through the data hierarchy efficiently.

Sample Prompt:

"What schemas or datasets are available in the tpch catalog?"

Example:

{
  "catalog": "tpch"
}

Response:

{
  "schemas": ["information_schema", "sf1", "sf100", "sf1000", "tiny"]
}

List all tables in a schema, giving you visibility into available datasets.

Sample Prompt:

"What tables are available in the tpch tiny schema? I need to know what data we can query."

Example:

{
  "catalog": "tpch",
  "schema": "tiny"
}

Response:

{
  "tables": ["customer", "lineitem", "nation", "orders", "part", "partsupp", "region", "supplier"]
}

Get the schema of a table, understanding the structure of your data for better query planning.

Sample Prompt:

"What columns are in the customer table? I need to know the data types and structure before writing my query."

Example:

{
  "catalog": "tpch",
  "schema": "tiny",
  "table": "customer"
}

Response:

{
  "columns": [
    {
      "name": "custkey",
      "type": "bigint",
      "nullable": false
    },
    {
      "name": "name",
      "type": "varchar",
      "nullable": false
    },
    {
      "name": "address",
      "type": "varchar",
      "nullable": false
    },
    {
      "name": "nationkey",
      "type": "bigint",
      "nullable": false
    },
    {
      "name": "phone",
      "type": "varchar",
      "nullable": false
    },
    {
      "name": "acctbal",
      "type": "double",
      "nullable": false
    },
    {
      "name": "mktsegment",
      "type": "varchar",
      "nullable": false
    },
    {
      "name": "comment",
      "type": "varchar",
      "nullable": false
    }
  ]
}

This information is invaluable for understanding the column names, data types, and nullability constraints before writing queries against the table.

Here's a complete interaction example showing how an AI assistant might use these tools to answer a business question:

User Query: "Can you help me analyze our biggest customers? I want to know the top 5 customers with the highest account balances."

AI Assistant's workflow:

1. First, discover available catalogs

   > Using list_catalogs tool
   > Discovers tpch catalog
   

2. Then, find available schemas

   > Using list_schemas tool with catalog "tpch"
   > Discovers "tiny" schema
   

3. Explore available tables

   > Using list_tables tool with catalog "tpch" and schema "tiny"
   > Finds "customer" table
   

4. Check the customer table schema

   > Using get_table_schema tool
   > Discovers "custkey", "name", "acctbal" and other columns
   

5. Finally, execute the query

   > Using execute_query tool with:
   > "SELECT custkey, name, acctbal FROM tpch.tiny.customer ORDER BY acctbal DESC LIMIT 5"
   

6. Returns the results to the user:

   The top 5 customers with highest account balances are:
   1. Customer #65 (Customer#000000065): $9,222.78
   2. Customer #13 (Customer#000000013): $8,270.47
   3. Customer #89 (Customer#000000089): $7,990.56
   4. Customer #11 (Customer#000000011): $7,912.91
   5. Customer #82 (Customer#000000082): $7,629.41
   

This seamless workflow demonstrates how the MCP tools enable AI assistants to explore and query data in a conversational manner.

The server can be configured using the following environment variables:

Note: When TRINO_SCHEME is set to "https", TRINO_SSL is automatically set to true regardless of the provided value.

Important: The default connection mode is HTTPS. If you're using an HTTP-only Trino server, you must set TRINO_SCHEME=http in your environment variables.

Security Note: By default, only read-only queries (SELECT, SHOW, DESCRIBE, EXPLAIN) are allowed to prevent SQL injection. If you need to execute write operations or other non-read queries, set TRINO_ALLOW_WRITE_QUERIES=true, but be aware this bypasses this security protection.

For Cursor Integration: When using with Cursor, set MCP_TRANSPORT=http and connect to the /sse endpoint. The server will automatically handle SSE (Server-Sent Events) connections.

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License - see the LICENSE file for details.

This project uses GitHub Actions for continuous integration and GoReleaser for automated releases.

Our CI pipeline performs the following checks on all PRs and commits to the main branch:

• Linting: Using golangci-lint to check for common code issues and style violations
• Go Module Verification: Ensuring go.mod and go.sum are properly maintained
• Formatting: Verifying code is properly formatted with gofmt

• Vulnerability Scanning: Using govulncheck to check for known vulnerabilities in dependencies
• Dependency Scanning: Using Trivy to scan for vulnerabilities in dependencies (CRITICAL, HIGH, and MEDIUM)
• SBOM Generation: Creating a Software Bill of Materials for dependency tracking
• SLSA Provenance: Creating verifiable build provenance for supply chain security

• Unit Tests: Running tests with race detection and code coverage reporting
• Build Verification: Ensuring the codebase builds successfully

• Least Privilege: Workflows run with minimum required permissions
• Pinned Versions: All GitHub Actions use specific versions to prevent supply chain attacks
• Dependency Updates: Automated dependency updates via Dependabot

When changes are merged to the main branch:

1. CI checks are run to validate code quality and security
2. If successful, a new release is automatically created with:
   • Semantic versioning based on commit messages
   • Binary builds for multiple platforms
   • Docker image publishing to GitHub Container Registry
   • SBOM and provenance attestation