ECS MCP Server Sample
The following sample demonstrates how to deploy an Agentic AI architecture using AWS Fargate for Amazon ECS. It uses AWS CDK to deploy the architecture components.
Alimenter votre agent
Installation de l'éditeur. Ce n'est pas un endpoint PlugMCP.
Host a copy with our keys and logs.
Pas encore de version packagée dans le registre. Suivez le README du dépôt.
URL distante ou commande sur votre machine ? Ce qui distingue les deux installations
Exécuter sur PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
LancerDe l'éditeur
The following sample demonstrates how to deploy an Agentic AI architecture using AWS Fargate for Amazon ECS. It uses AWS CDK to deploy the architecture components.
This sample demonstrates how to deploy an Agentic AI architecture using AWS Fargate for Amazon ECS with AWS CDK. The sample features an AI Service that connects to multiple Model Context Protocol (MCP) servers to perform various actions
The architecture consists of the following components:
Service-to-service communication between AWS Fargate services is done using Service Connect. The AI Service leverages custom DNS names to connect to the MCP Servers available in this sample.
The sample also generates an AWS Secret to be used as the x-api-key header when calling the Application Load Balancer URLs.
To deploy this sample in your account follow the instructions below:
# install dependencies
$ npm install
# login to the public ECR registry to allow download base images
$ aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws
# bootstrap your AWS environment (in case you haven't done it yet)
$ npm run cdk bootstrap
# deploy the CDK Stack into your account
$ npm run cdk deploy
Once the deployment is finalized in your account, you will have the following output from AWS CDK:
✅ EcsMcpServerSampleStack
✨ Deployment time: 362.32s
Outputs:
EcsMcpServerSampleStack.ALBLoadBalancerDNS6ED38D87 = [application-load-balancer-url]
EcsMcpServerSampleStack.AiServiceSecretARN2FB050DB = [secret-arn]
Before invoking the Application Load Balancer URL, you'll need to retrieve the secret value with the following command:
$ aws secretsmanager get-secret-value --secret-id [secret-arn]
# or use the following to directly get the value
$ aws secretsmanager get-secret-value --secret-id [secret-arn] --output json | jq -r '.SecretString'
Now that you have all the information, you can call the Application Load Balancer URL to test the agent:
curl --location 'http://[ALB-URL]/chat' \
--header 'x-api-key: [secret-value]' \
--header 'Content-Type: application/json' \
--data '{
"prompt": "what'\''s the command to list AWS Buckets"
}'
{
"answer": "The command to list AWS S3 buckets is:\n\n```bash\naws s3 ls\n```\n\nThis command will display all S3 buckets in your AWS account, showing:\n- Creation date and time\n- Bucket name\n\nAs you can see from the output, it lists all the buckets with their creation timestamps and names. The command works across all regions and shows buckets from your entire AWS account.\n"
}
or execute the AWS CLI command:
curl --location 'http://[ALB-URL]/chat' \
--header 'x-api-key: [secret-value]' \
--header 'Content-Type: application/json' \
--data '{
"prompt": "list all my AWS Buckets"
}'
{
"answer": "Here are all your S3 buckets: [omissis]"
}
or compute some math using the custom MCP Server:
curl --location 'http://[ALB-URL]/chat' \
--header 'x-api-key: [secret-value]' \
--header 'Content-Type: application/json' \
--data '{
"prompt": "what'\''s 2+2"
}'
{
"answer": "2 + 2 = **4**\n"
}
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.
This sample demonstrates how to deploy an Agentic AI architecture using AWS Fargate for Amazon ECS with AWS CDK. The sample features an AI Service that connects to multiple Model Context Protocol (MCP
Vue d'ensemble
This sample demonstrates how to deploy an Agentic AI architecture using AWS Fargate for Amazon ECS with AWS CDK. The sample features an AI Service that connects to multiple Model Context Protocol (MCP) servers to perform various actions
Outils
Pas encore de liste d'outils. Les serveurs distants en obtiennent une après le premier contrôle d'état.
FAQ
Copiez sur cette page la configuration stdio pour Cursor, Claude Desktop, Claude Code ou VS Code. Elle utilise l'installation de l'éditeur, pas un proxy PlugMCP.
Aucune variable d'environnement requise n'est indiquée sur cette fiche.
Sample Ecs s'exécute en local via stdio.
Dernière vérification : 2026-10-28. This sample demonstrates how to deploy an Agentic AI architecture using AWS Fargate for Amazon ECS with AWS CDK. The sample features an AI Service that connects to multiple Model Context Protocol (MCP) servers to perform various actions
