Improve AI-assisted growth with Amazon ECS, Amazon EKS and AWS Serverless MCP server

Immediately, we’re introducing specialised Mannequin Context Protocol (MCP) servers for Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), and AWS Serverless, now accessible within the AWS Labs GitHub repository. These open supply options prolong AI growth assistants capabilities with real-time, contextual responses that transcend their pre-trained information. Whereas Giant Language Fashions (LLM) inside AI assistants depend on public documentation, MCP servers ship present context and service-specific steerage that can assist you stop widespread deployment errors and supply extra correct service interactions.

You need to use these open supply options to develop purposes quicker, utilizing up-to-date information of Amazon Internet Companies (AWS) capabilities and configurations in the course of the construct and deployment course of. Whether or not you’re writing code in your built-in growth atmosphere (IDE), or debugging manufacturing points, these MCP servers help AI code assistants with deep understanding of Amazon ECS, Amazon EKS, and AWS Serverless capabilities, accelerating the journey from code to manufacturing. They work with widespread AI-enabled IDEs, together with Amazon Q Developer on the command line (CLI), that can assist you construct and deploy purposes utilizing pure language instructions.

• The Amazon ECS MCP Server containerizes and deploys purposes to Amazon ECS inside minutes by configuring all related AWS assets, together with load balancers, networking, auto-scaling, monitoring, Amazon ECS process definitions, and providers. Utilizing pure language directions, you’ll be able to handle cluster operations, implement auto-scaling methods, and use real-time troubleshooting capabilities to determine and resolve deployment points rapidly.
• For Kubernetes environments, the Amazon EKS MCP Server offers AI assistants with up-to-date, contextual details about your particular EKS atmosphere. It provides entry to the newest EKS options, information base, and cluster state info. This provides AI code assistants extra correct, tailor-made steerage all through the applying lifecycle, from preliminary setup to manufacturing deployment.
• The AWS Serverless MCP Server enhances the serverless growth expertise by offering AI coding assistants with complete information of serverless patterns, greatest practices, and AWS providers. Utilizing AWS Serverless Software Mannequin Command Line Interface (AWS SAM CLI) integration, you’ll be able to deal with occasions and deploy infrastructure whereas implementing confirmed architectural patterns. This integration streamlines operate lifecycles, service integrations, and operational necessities all through your software growth course of. The server additionally offers contextual steerage for infrastructure as code choices, AWS Lambda particular greatest practices, and occasion schemas for AWS Lambda occasion supply mappings.

Let’s see it in motion
If that is your first time utilizing AWS MCP servers, go to the Set up and Setup information within the AWS Labs GitHub repository to set up directions. As soon as put in, add the next MCP server configuration to your native setup:

Set up Amazon Q for command line and add the configuration to ~/.aws/amazonq/mcp.json. Should you’re already an Amazon Q CLI consumer, add solely the configuration.

{
  "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 this demo I’m going to make use of the Amazon Q CLI to create an software that understands video utilizing 02_using_converse_api.ipynb from Amazon Nova mannequin cookbook repository as pattern code. To do that, I ship the next immediate:

I wish to create a backend software that mechanically extracts metadata and understands the content material of photographs and movies uploaded to an S3 bucket and shops that info in a database. I would like to make use of a serverless system for processing. Might you generate every little thing I would like, together with the code and instructions or steps to arrange the mandatory infrastructure, for it to work from begin to end? - Use 02_using_converse_api.ipynb as instance code for the picture and video understanding.

Amazon Q CLI identifies the mandatory instruments, together with the MCP serverawslabs.aws-serverless-mcp-server. Via a single interplay, the AWS Serverless MCP server determines all necessities and greatest practices for constructing a strong structure.

I ask to Amazon Q CLI that construct and take a look at the applying, however encountered an error. Amazon Q CLI rapidly resolved the difficulty utilizing accessible instruments. I verified success by checking the document created within the Amazon DynamoDB desk and testing the applying with the dog2.jpeg file.

To boost video processing capabilities, I made a decision emigrate my media evaluation software to a containerized structure. I used this immediate:

I would such as you to create a easy software just like the media evaluation one, however as a substitute of being serverless, it ought to be containerized. Please assist me construct it in a brand new CDK stack.

Amazon Q Developer begins constructing the applying. I took benefit of this time to seize a espresso. Once I returned to my desk, espresso in hand, I used to be pleasantly stunned to search out the applying prepared. To make sure every little thing was as much as present requirements, I merely requested:

please evaluate the code and all app utilizing the awslabsecs_mcp_server instruments 

Amazon Q Developer CLI offers me a abstract with all of the enhancements and a conclusion.

I ask it to make all the mandatory modifications, as soon as prepared I ask Amazon Q developer CLI to deploy it in my account, all utilizing pure language.

After a couple of minutes, I evaluate that I’ve an entire containerized software from the S3 bucket to all the mandatory networking.

I ask Amazon Q developer CLI to check the app ship it the-sea.mp4 video file and obtained a timed out error, so Amazon Q CLI decides to make use of the fetch_task_logs from awslabsecs_mcp_server instrument to evaluate the logs, determine the error after which repair it.

After a brand new deployment, I strive it once more, and the applying efficiently processed the video file

I can see the data in my Amazon DynamoDB desk.

To check the Amazon EKS MCP server, I’ve code for an online app within the auction-website-main folder and I wish to construct an online strong app, for that I requested Amazon Q CLI to assist me with this immediate:

Create an online software utilizing the prevailing code within the auction-website-main folder. This software will develop, so I want to create it in a brand new EKS cluster

As soon as the Docker file is created, Amazon Q CLI identifies generate_app_manifests from awslabseks_mcp_server as a dependable instrument to create a Kubernetes manifests for the applying.

Then create a brand new EKS cluster utilizing the manage_eks_staks instrument.

As soon as the app is prepared, the Amazon Q CLI deploys it and offers me a abstract of what it created.

I can see the cluster standing within the console.

After a couple of minutes and resolving a few points utilizing the search_eks_troubleshoot_guide instrument the applying is able to use.

Now I’ve a Kitties market net app, deployed on Amazon EKS utilizing solely pure language instructions by means of Amazon Q CLI.

Get began immediately
Go to the AWS Labs GitHub repository to begin utilizing these AWS MCP servers and improve your AI-powered developmen there. The repository contains implementation guides, instance configurations, and extra specialised servers to run AWS Lambda operate, which transforms your present AWS Lambda features into AI-accessible instruments with out code modifications, and Amazon Bedrock Information Bases Retrieval MCP server, which offers seamless entry to your Amazon Bedrock information bases. Different AWS specialised servers within the repository embrace documentation, instance configurations, and implementation guides to start constructing purposes with higher pace and reliability.

To study extra about MCP Servers for AWS Serverless and Containers and the way they will remodel your AI-assisted software growth, go to the Introducing AWS Serverless MCP Server: AI-powered growth for contemporary purposes, Automating AI-assisted container deployments with the Amazon ECS MCP Server, and Accelerating software growth with the Amazon EKS MCP server deep-dive blogs.

— Eli