Redshift MCP Server

by Moonlight-CL
Apache 2.0

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
-
security - not tested
A
license - permissive license
-
quality - not tested

A Model Context Protocol server that enables AI assistants to interact with Amazon Redshift databases, allowing for schema exploration, query execution, and statistics collection.

  1. Introduction
    1. Installation
      1. Prerequisites
      2. Install from source
    2. Configuration
      1. Usage
        1. Starting the server
        2. Integrating with AI assistants
      2. Features
        1. Resources
        2. Tools
      3. Examples
        1. Listing schemas
        2. Listing tables in a schema
        3. Getting table DDL
        4. Executing SQL
        5. Analyzing a table
        6. Getting execution plan
      4. Development
        1. Project structure
        2. Dependencies

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