AWS MCP Servers: Turn Your AI Assistant Into an AWS Expert

Stop wrestling with outdated AWS documentation in your AI assistant. These specialized MCP servers give your coding buddy direct access to current AWS docs, best practices, and automated workflows—so you can build cloud infrastructure that actually works.

The Problem Every AWS Developer Knows

Your AI assistant hallucinates AWS APIs that don't exist, suggests deprecated services, and generates Infrastructure as Code that fails deployment. Meanwhile, you're constantly context-switching between your IDE, AWS docs, and the console just to verify what your assistant is telling you.

The root cause? Foundation models are trained on static data that's months or years old. They don't know about the latest AWS services, API changes, or current best practices.

What AWS MCP Servers Actually Do

Instead of feeding your AI assistant stale training data, these MCP servers provide real-time connections to:

• Live AWS Documentation: Latest API references, service guides, and SDK docs
• Automated Workflows: CDK, Terraform, CloudFormation, and SAM operations with security scanning
• Direct Service Access: Query DynamoDB, manage Lambda functions, analyze costs, generate diagrams
• Enterprise Knowledge: Bedrock Knowledge Bases, Kendra search, and document analysis

Your AI assistant stops guessing and starts knowing.

Real Productivity Gains

Infrastructure as Code: Ask "Create a CDK stack for a serverless API with DynamoDB" and get current CDK v2 syntax with security best practices built in, not outdated examples from 2022.

Cost Optimization: "What will this Terraform plan cost me?" gets you accurate pre-deployment estimates, not generic advice about "monitoring your spend."

Debugging: "Why is my Lambda function timing out?" queries your actual CloudWatch logs and gives you specific troubleshooting steps based on your error patterns.

Architecture: "Design a microservices architecture for this use case" generates proper AWS architecture diagrams with current service recommendations.

The Complete Toolkit

For Active Development:

• Core MCP Server (intelligent planning and orchestration)
• AWS Documentation Server (live docs and API references)
• CDK/Terraform/CloudFormation Servers (IaC with security scanning)
• Git Repo Research Server (semantic code search)

For Data Operations:

• DynamoDB, Aurora, DocumentDB, Neptune servers
• ElastiCache, OpenSearch, Timestream servers
• Direct database operations via RDS Data API

For AI-Enhanced Workflows:

• Bedrock Knowledge Bases (RAG with citations)
• Nova Canvas (AI image generation)
• Synthetic Data Generation
• Document/media analysis

For Production Operations:

• Cost Analysis and Cost Explorer servers
• CloudWatch Logs analysis
• Lambda function execution
• Step Functions workflow automation

Integration That Just Works

These servers integrate with the AI assistants you're already using:

• Cline: Full MCP support with AWS Bedrock integration
• Cursor: Project-specific or global MCP configuration
• Windsurf: Built-in MCP server management
• Amazon Q Developer: Native MCP support
• Claude Desktop: Standard MCP client support

Installation is straightforward—add servers to your MCP config and they're immediately available to your AI assistant.

Quick Start

1. Install uv and configure your AWS credentials
2. Add servers to your MCP client configuration:

{
  "mcpServers": {
    "awslabs.core-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.core-mcp-server@latest"]
    },
    "awslabs.aws-documentation-mcp-server": {
      "command": "uvx", 
      "args": ["awslabs.aws-documentation-mcp-server@latest"]
    },
    "awslabs.cdk-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.cdk-mcp-server@latest"]
    }
  }
}

3. Start coding—your AI assistant now has direct access to current AWS knowledge and tools

Why This Matters Now

AI-assisted development is moving fast, but most AI assistants are still working with stale cloud knowledge. AWS MCP Servers close that gap by providing real-time access to the AWS ecosystem.

The result? You spend less time fact-checking your AI assistant and more time building. Your infrastructure code works on the first deployment. Your cost estimates are accurate. Your debugging sessions are shorter.

Get the AWS MCP Servers: GitHub Repository | Documentation

Turn your AI coding assistant into an AWS expert that actually knows what it's talking about.