AWS has released a set of open-source Model Context Protocol (MCP) servers on GitHub for Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), and AWS Serverless. These are specialized servers that enhance the capabilities of AI development assistants, such as Amazon Q Developer, by providing them with real-time, contextual information specific to these AWS services.
While Large Language Models (LLMs) within AI assistants typically rely on general public documentation, these MCP servers offer current context and service-specific guidance. Hence, developers can receive more accurate assistance and proactively prevent common deployment errors when building and deploying applications on AWS.
Hariharan Eswaran concluded in a Medium blog post:
The launch of MCP servers is about empowering developers with tools that keep up with the complexity of modern cloud-native apps. Whether you’re deploying containers, managing Kubernetes, or going serverless, MCP servers let your AI assistant manage infrastructure like a team member — not just a chatbot.
Furthermore, according to the company, leveraging these open-source solutions allows developers to accelerate their application development process by utilizing up-to-date knowledge of AWS capabilities and configurations directly within their integrated development environment (IDE) or command-line interface (CLI). Moreover, the key features and benefits include:
- Amazon ECS MCP Server: Simplifies containerized application deployment to Amazon ECS by configuring necessary AWS resources like load balancers, networking, auto-scaling, and task definitions using natural language. It also aids in cluster operations and real-time troubleshooting.
- Amazon EKS MCP Server: Provides AI assistants with up-to-date, contextual information about specific EKS environments, including the latest features, knowledge base, and cluster state. This enables more tailored guidance throughout the Kubernetes application lifecycle.
- AWS Serverless MCP Server: Enhances the serverless development experience by offering comprehensive knowledge of serverless patterns, best practices, and AWS services. Integration with the AWS Serverless Application Model Command Line Interface (AWS SAM CLI) streamlines function lifecycles and infrastructure deployment. It also provides contextual guidance for Infrastructure as Code decisions and best practices for AWS Lambda.
The announcement details practical examples of using the MCP servers with Amazon Q CLI to build and deploy applications for media analysis (serverless and containerized on ECS) and a web application on EKS, all through natural language commands. The examples showcase the AI assistant’s ability to identify necessary tools, generate configurations, troubleshoot errors, and even review code based on the contextual information provided by the MCP servers.
The announcement has already garnered positive attention from the developer community. Maniganda, commenting on a LinkedIn post, expressed enthusiasm:
The ability for AI to interact with AWS compute services in real-time will undoubtedly streamline operations and enhance efficiency. I’m looking forward to seeing how the open-source framework evolves and the impact it will have on Kubernetes management.
Users can get started by visiting the AWS Labs GitHub repository for installation guides and configurations. The repository also includes MCP servers for transforming existing AWS Lambda functions into AI-accessible tools and for accessing Amazon Bedrock Knowledge Bases. Deep-dive blogs are available for those wanting to learn more about the individual MCP servers for AWS Serverless, Amazon ECS, and Amazon EKS.
