Amazon ECS MCP Server

What is Amazon ECS MCP Server?

The Amazon ECS MCP Server is an integration module between AI models and Amazon Elastic Container Service (ECS). It exposes a set of MCP tools that allow AI coding assistants and agentic systems to automatically generate Dockerfiles, orchestrate ECS resources, deploy containerized applications, set up AWS networking components like Application Load Balancers (ALBs), and provide troubleshooting guidance within an MCP-enabled environment. This enables streamlined, AI-assisted containerization and deployment workflows for cloud-native development on AWS.

How to Configure

1. Prerequisites:

   • Install Python (recommended 3.10) and uv package manager (installation guide).
   • Set up your AWS credentials—either using AWS Profiles or environment variables, ensuring access rights to ECS and related AWS services.

2. Server Setup:

   • Install awslabs.ecs-mcp-server via uvx, e.g.:
     uvx awslabs.ecs-mcp-server@latest

   • Or configure your MCP client (such as Amazon Q CLI, Cline, Cursor, Windsurf, etc.) to run the ECS MCP Server using a JSON configuration, for example:

     {
       "mcpServers": {
         "awslabs.ecs-mcp-server": {
           "command": "uvx",
           "args": ["awslabs.ecs-mcp-server@latest"],
           "env": {
             "AWS_PROFILE": "your-aws-profile",
             "AWS_REGION": "us-east-1",
             "FASTMCP_LOG_LEVEL": "ERROR"
           }
         }
       }
     }
     

   • For Dockerized setups, use the provided Docker image with mapped AWS credentials and desired environment variables.

3. Enable in Your Client:

   • Add the ECS MCP Server to your client’s configuration, following the respective documentation for tools like Cursor, Cline, or Windsurf.

How to Use

1. List Available Tools:
   Use your AI assistant’s interface or MCP client command to enumerate ECS MCP tools (usually via the tools/list endpoint or interface options). The available tools correspond to key ECS operations (see below).

2. Invoke Tool Functions:
   Request your AI model or assistant to perform ECS tasks in natural language, e.g.:

   • “Containerize my web application and set up ECS deployment.”
   • “List the running ECS services in cluster X.”
   • “Generate a Dockerfile for my Node.js project and deploy with an ALB.” The assistant will call the appropriate MCP tool endpoints, passing arguments and returning operation results or guidance in the chat or output interface.

3. Interactive Troubleshooting:
   When facing deployment issues, prompt the assistant to diagnose ECS problems—it will use ECS MCP tools to fetch service/task statuses, logs, and suggest remediations.

4. Resource Management & Exploration:
   Ask for overviews of task definitions, clusters, services, and running tasks. Use AI-driven recommendations to improve security and deployment setups.

Key Features

• Automated Containerization:
  Generates Dockerfiles and container config for various language runtimes and frameworks.

• AI-assisted ECS Deployment:
  Manages the creation of ECS clusters, services, task definitions, networking (VPC, ALB), and IAM roles.

• Resource Discovery & Management:
  Lists and explores ECS resources—clusters, services, task definitions, and live tasks.

• Integrated Troubleshooting:
  Diagnoses deployment issues, fetches event and log information, and recommends fixes.

• Best Practices Guidance:
  Offers AWS security best practices and optimization recommendations for container deployments.

• Seamless Tool Integration:
  Leverages the Model Context Protocol for compatibility with multiple AI assistants and development environments.

Use Cases

• Fast Onboarding to ECS:
  New teams and developers can automatically generate container artifacts and create ECS infrastructure for new or legacy applications.

• AI-driven DevOps Automation:
  Coding assistants automate routine operations—deployments, scaling, rollbacks, and ECS resource analysis.

• Continuous Delivery Pipelines:
  Use as a backend for AI-powered code assistants that manage CI/CD workflows, leveraging ECS deployments.

• Operational Troubleshooting:
  Quickly diagnose and remediate ECS task failures using AI-driven chat.

• Cloud Migration Projects:
  Streamline containerization and deployment when migrating on-prem or VM-based workloads to AWS ECS.

Frequently Asked Questions

Q1: Which AWS permissions are required to use Amazon ECS MCP Server?
A1: The MCP server requires permissions for ECS (clusters, services, tasks), IAM (roles), networking (VPC, ALB), and ECR as needed. Grant your AWS profile or credentials the IAM policies necessary for ECS administration.

Q2: Can this server deploy both EC2 and Fargate ECS services?
A2: Yes, you can use the ECS MCP Server to deploy ECS services on both Fargate and EC2 launch types, depending on your workload requirements and configuration.

Q3: How does the server help with troubleshooting deployments?
A3: The server exposes tools to fetch ECS event logs, task and service statuses, and provides AI-assisted diagnostics to identify issues such as image pull errors, networking misconfigurations, or insufficient permissions.

Q4: Is it possible to customize the generated Dockerfile and ECS task definition?
A4: Yes, you can prompt the AI assistant with additional configuration requirements, and it will generate or modify Dockerfiles and task definitions according to your specifications.

Q5: Can I use this with existing ECS clusters and services?
A5: Absolutely. You can list or fetch details from any ECS cluster or service accessible via your AWS credentials and manage or update them using appropriate tool calls.