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In the present day, 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 obtainable within the AWS Labs GitHub repository. These open supply options lengthen AI growth assistants capabilities with real-time, contextual responses that transcend their pre-trained data. Whereas Giant Language Fashions (LLM) inside AI assistants depend on public documentation, MCP servers ship present context and service-specific steering that can assist you forestall widespread deployment errors and supply extra correct service interactions.
You should utilize these open supply options to develop purposes sooner, utilizing up-to-date data of Amazon Internet Companies (AWS) capabilities and configurations throughout the construct and deployment course of. Whether or not you’re writing code in your built-in growth surroundings (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 common 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 job definitions, and companies. Utilizing pure language directions, you may handle cluster operations, implement auto-scaling methods, and use real-time troubleshooting capabilities to determine and resolve deployment points shortly.
- For Kubernetes environments, the Amazon EKS MCP Server offers AI assistants with up-to-date, contextual details about your particular EKS surroundings. It presents entry to the newest EKS options, data base, and cluster state data. This provides AI code assistants extra correct, tailor-made steering 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 data of serverless patterns, finest practices, and AWS companies. Utilizing AWS Serverless Software Mannequin Command Line Interface (AWS SAM CLI) integration, you may 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 steering for infrastructure as code selections, AWS Lambda particular finest 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. In the event 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 robotically extracts metadata and understands the content material of photos and movies uploaded to an S3 bucket and shops that data in a database. I might like to make use of a serverless system for processing. Might you generate every thing I want, together with the code and instructions or steps to arrange the required 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 required instruments, together with the MCP serverawslabs.aws-serverless-mcp-server. By means of a single interplay, the AWS Serverless MCP server determines all necessities and finest practices for constructing a sturdy structure.
I ask to Amazon Q CLI that construct and take a look at the applying, however encountered an error. Amazon Q CLI shortly resolved the difficulty utilizing obtainable instruments. I verified success by checking the report created within the Amazon DynamoDB desk and testing the applying with the dog2.jpeg file.
To reinforce video processing capabilities, I made a decision emigrate my media evaluation software to a containerized structure. I used this immediate:
I might such as you to create a easy software just like the media evaluation one, however as an alternative of being serverless, it needs 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. After I returned to my desk, espresso in hand, I used to be pleasantly shocked to search out the applying prepared. To make sure every thing was as much as present requirements, I merely requested:
please overview 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 required 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 overview that I’ve a whole containerized software from the S3 bucket to all the required networking.
I ask Amazon Q developer CLI to check the app ship it the-sea.mp4 video file and acquired a timed out error, so Amazon Q CLI decides to make use of the fetch_task_logs from awslabsecs_mcp_server device to overview 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 information in my Amazon DynamoDB desk.
To check the Amazon EKS MCP server, I’ve code for an internet app within the auction-website-main folder and I wish to construct an internet strong app, for that I requested Amazon Q CLI to assist me with this immediate:
Create an internet software utilizing the present 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 device to create a Kubernetes manifests for the applying.
Then create a brand new EKS cluster utilizing the manage_eks_staks device.
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 device the applying is able to use.
Now I’ve a Kitties market internet app, deployed on Amazon EKS utilizing solely pure language instructions by means of Amazon Q CLI.
Get began right now
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 current AWS Lambda features into AI-accessible instruments with out code modifications, and Amazon Bedrock Data Bases Retrieval MCP server, which offers seamless entry to your Amazon Bedrock data bases. Different AWS specialised servers within the repository embrace documentation, instance configurations, and implementation guides to start constructing purposes with higher velocity 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
