Boost AI Development Speed with AWS MCP Servers for ECS, EKS, and Serverless

Enhance AI-Assisted Development with Amazon ECS, Amazon EKS, and AWS Serverless MCP Servers

Amazon has introduced specialized Model Context Protocol (MCP) servers for Amazon Elastic Container Service (ECS), Amazon Elastic Kubernetes Service (EKS), and AWS Serverless environments. These open source solutions, available in the AWS Labs GitHub repository, provide AI development assistants with real-time, contextual responses that extend beyond their static pre-trained knowledge.

While Large Language Models (LLMs) typically rely on public documentation, MCP servers supply current context and service-specific guidance. This helps prevent common deployment errors and improves service interactions accuracy.

Accelerate Development with Context-Aware AI Assistance

These MCP servers allow faster application development by providing up-to-date AWS capabilities and configuration knowledge during coding and deployment. Whether you’re coding in an IDE or debugging production issues, MCP servers empower AI code assistants with deep insights into Amazon ECS, Amazon EKS, and AWS Serverless features.

The servers integrate with popular AI-enabled IDEs and tools like the Amazon Q Developer CLI, enabling you to build and deploy applications using natural language commands.

Amazon ECS MCP Server

The Amazon ECS MCP Server helps containerize and deploy applications to Amazon ECS within minutes. It configures all relevant AWS resources such as load balancers, networking, auto-scaling, monitoring, task definitions, and services automatically. Using natural language, you can manage cluster operations, implement auto-scaling strategies, and troubleshoot deployment issues in real time.

Amazon EKS MCP Server

The Amazon EKS MCP Server equips AI assistants with up-to-date information about your specific Kubernetes environment. It provides access to the latest EKS features, cluster state, and knowledge base. This enables AI tools to offer precise guidance from initial setup to production deployment, improving the application lifecycle management on EKS clusters.

AWS Serverless MCP Server

The AWS Serverless MCP Server enhances serverless development by offering AI assistants comprehensive knowledge of serverless design patterns, best practices, and AWS services. It integrates with AWS SAM CLI to manage events and deploy infrastructure while implementing proven architectural patterns.

This server supports function lifecycles, service integrations, and operational requirements, delivering contextual guidance on infrastructure as code, AWS Lambda best practices, and event schemas for Lambda event sources.

See It in Action

To get started, follow the Installation and Setup guide on the AWS Labs GitHub repository. After installation, add the MCP server configuration to your local setup as shown:

{
  "mcpServers": {
    "awslabs.aws-serverless-mcp":  {
      "command": "uvx",
      "timeout": 60,
      "args": ["awslabs.aws-serverless_mcp_server@latest"],
    },
    "awslabs.ecs-mcp-server": {
      "disabled": false,
      "command": "uv",
      "timeout": 60,
      "args": ["awslabs.ecs-mcp-server@latest"],
    },
    "awslabs.eks-mcp-server": {
      "disabled": false,
      "timeout": 60,
      "command": "uv",
      "args": ["awslabs.eks-mcp-server@latest"],
    }
  }
}

For example, using the Amazon Q CLI with a sample from the Amazon Nova model cookbook, you can create a backend application that extracts metadata from images and videos uploaded to an S3 bucket. The Serverless MCP server guides the setup of all required infrastructure and generates the necessary code. When errors occur, the MCP server helps quickly identify and resolve them, streamlining the testing process.

To scale your media analysis application, you can migrate it to a containerized architecture. Using a natural language prompt, the Amazon Q Developer CLI builds the containerized app in a new CDK stack. It reviews the code using ECS MCP server tools, applies improvements, and handles deployment—all through natural language commands.

For Kubernetes deployments, the EKS MCP server supports building and deploying applications on new EKS clusters. It generates Kubernetes manifests, manages cluster creation, and assists in troubleshooting. This lets you deploy complex applications like a marketplace web app with minimal manual intervention.

Get Started Today

Explore the AWS Labs GitHub repository to access these MCP servers, implementation guides, and example configurations. The repository also includes specialized servers for AWS Lambda functions and Amazon Bedrock knowledge bases, enabling AI-powered access without code changes.

To deepen your AWS and AI development skills, check out relevant courses on Complete AI Training. These resources provide practical knowledge to build applications more efficiently and confidently using AI-assisted tools.