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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 obtainable within the AWS Labs GitHub repository. These open supply options prolong AI improvement assistants capabilities with real-time, contextual responses that transcend their pre-trained data. Whereas Massive Language Fashions (LLM) inside AI assistants depend on public documentation, MCP servers ship present context and service-specific steering that will help you stop frequent deployment errors and supply extra correct service interactions.
You should use these open supply options to develop functions quicker, utilizing up-to-date data of Amazon Internet Providers (AWS) capabilities and configurations through the construct and deployment course of. Whether or not you’re writing code in your built-in improvement setting (IDE), or debugging manufacturing points, these MCP servers assist 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 will help you construct and deploy functions utilizing pure language instructions.
- The Amazon ECS MCP Server containerizes and deploys functions to Amazon ECS inside minutes by configuring all related AWS assets, together with load balancers, networking, auto-scaling, monitoring, Amazon ECS process definitions, and companies. Utilizing pure language directions, you possibly can 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 supplies AI assistants with up-to-date, contextual details about your particular EKS setting. It provides entry to the newest EKS options, data base, and cluster state info. This provides AI code assistants extra correct, tailor-made steering all through the appliance lifecycle, from preliminary setup to manufacturing deployment.
- The AWS Serverless MCP Server enhances the serverless improvement expertise by offering AI coding assistants with complete data of serverless patterns, greatest practices, and AWS companies. Utilizing AWS Serverless Software Mannequin Command Line Interface (AWS SAM CLI) integration, you possibly can deal with occasions and deploy infrastructure whereas implementing confirmed architectural patterns. This integration streamlines perform lifecycles, service integrations, and operational necessities all through your software improvement course of. The server additionally supplies contextual steering 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. When 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 photos 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. May you generate every part 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. 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 check the appliance, however encountered an error. Amazon Q CLI rapidly resolved the difficulty utilizing obtainable instruments. I verified success by checking the report created within the Amazon DynamoDB desk and testing the appliance 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 must be containerized. Please assist me construct it in a brand new CDK stack.
Amazon Q Developer begins constructing the appliance. I took benefit of this time to seize a espresso. Once I returned to my desk, espresso in hand, I used to be pleasantly shocked to search out the appliance prepared. To make sure every part was as much as present requirements, I merely requested:
please assessment the code and all app utilizing the awslabsecs_mcp_server instruments
Amazon Q Developer CLI provides 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 assessment that I’ve an entire 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 obtained a timed out error, so Amazon Q CLI decides to make use of the fetch_task_logs from awslabsecs_mcp_server instrument to assessment the logs, determine the error after which repair it.
After a brand new deployment, I strive it once more, and the appliance 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 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 instrument to create a Kubernetes manifests for the appliance.
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 provides 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 appliance is able to use.
Now I’ve a Kitties market internet app, deployed on Amazon EKS utilizing solely pure language instructions by Amazon Q CLI.
Get began in the present day
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 perform, which transforms your present AWS Lambda features into AI-accessible instruments with out code modifications, and Amazon Bedrock Data Bases Retrieval MCP server, which supplies 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 functions with larger velocity and reliability.
To be taught extra about MCP Servers for AWS Serverless and Containers and the way they’ll rework your AI-assisted software improvement, go to the Introducing AWS Serverless MCP Server: AI-powered improvement for contemporary functions, Automating AI-assisted container deployments with the Amazon ECS MCP Server, and Accelerating software improvement with the Amazon EKS MCP server deep-dive blogs.
— Eli
