Redshift MCP Server

A Model Context Protocol (MCP) server for Amazon Redshift that enables AI assistants to interact with Redshift databases.

Introduction

Redshift MCP Server is a Python-based implementation of the Model Context Protocol that provides tools and resources for interacting with Amazon Redshift databases. It allows AI assistants to:

• List schemas and tables in a Redshift database
• Retrieve table DDL (Data Definition Language) scripts
• Get table statistics
• Execute SQL queries
• Analyze tables to collect statistics information
• Get execution plans for SQL queries

Installation

Prerequisites

• Python 3.13 or higher
• Amazon Redshift cluster
• Redshift credentials (host, port, username, password, database)

Install from source

# Clone the repository
git clone https://github.com/Moonlight-CL/redshift-mcp-server.git
cd redshift-mcp-server

# Install dependencies
uv sync

Configuration

The server requires the following environment variables to connect to your Redshift cluster:

RS_HOST=your-redshift-cluster.region.redshift.amazonaws.com
RS_PORT=5439
RS_USER=your_username
RS_PASSWORD=your_password
RS_DATABASE=your_database
RS_SCHEMA=your_schema  # Optional, defaults to "public"

You can set these environment variables directly or use a `.env` file.

Usage

Starting the server

# Start the server
uv run --with mcp python-dotenv redshift-connector mcp
mcp run src/redshift_mcp_server/server.py

Integrating with AI assistants

To use this server with an AI assistant that supports MCP, add the following configuration to your MCP settings:

{
  "mcpServers": {
    "redshift": {
      "command": "uv",
      "args": ["--directory", "src/redshift_mcp_server", "run", "server.py"],
      "env": {
        "RS_HOST": "your-redshift-cluster.region.redshift.amazonaws.com",
        "RS_PORT": "5439",
        "RS_USER": "your_username",
        "RS_PASSWORD": "your_password",
        "RS_DATABASE": "your_database",
        "RS_SCHEMA": "your_schema"
      }
    }
  }
}

Features

Resources

The server provides the following resources:

• `rs:///schemas` - Lists all schemas in the database
• `rs:///{schema}/tables` - Lists all tables in a specific schema
• `rs:///{schema}/{table}/ddl` - Gets the DDL script for a specific table
• `rs:///{schema}/{table}/statistic` - Gets statistics for a specific table

Tools

The server provides the following tools:

• `execute_sql` - Executes a SQL query on the Redshift cluster
• `analyze_table` - Analyzes a table to collect statistics information
• `get_execution_plan` - Gets the execution plan with runtime statistics for a SQL query

Examples

Listing schemas

access_mcp_resource("redshift-mcp-server", "rs:///schemas")

Listing tables in a schema

access_mcp_resource("redshift-mcp-server", "rs:///public/tables")

Getting table DDL

access_mcp_resource("redshift-mcp-server", "rs:///public/users/ddl")

Executing SQL

use_mcp_tool("redshift-mcp-server", "execute_sql", {"sql": "SELECT * FROM public.users LIMIT 10"})

Analyzing a table

use_mcp_tool("redshift-mcp-server", "analyze_table", {"schema": "public", "table": "users"})

Getting execution plan

use_mcp_tool("redshift-mcp-server", "get_execution_plan", {"sql": "SELECT * FROM public.users WHERE user_id = 123"})

Development

Project structure

redshift-mcp-server/
├── src/
│   └── redshift_mcp_server/
│       ├── __init__.py
│       └── server.py
├── pyproject.toml
└── README.md

Dependencies

• `mcp[cli]>=1.5.0` - Model Context Protocol SDK
• `python-dotenv>=1.1.0` - For loading environment variables from .env files
• `redshift-connector>=2.1.5` - Python connector for Amazon Redshift