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MCP Server on EKS

@jbrcoleman

About MCP Server on EKS

Demo eks mcp server

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-server-eks-jbrcoleman": {
      "command": "python",
      "args": [
        "server-enhanced.py"
      ]
    }
  }
}

Tools

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Overview

What is MCP Server on EKS?

This project deploys a Model Context Protocol (MCP) server on Amazon EKS using Terraform for infrastructure management. It provides natural-language and programmatic access to Kubernetes cluster operations, targeting developers and platform teams who manage AWS EKS clusters.

How to use MCP Server on EKS?

Deploy via the provided scripts in phases (infrastructure, Karpenter, app) or all at once using ./scripts/deploy.sh all. For legacy deployment without Karpenter, use make init plan apply then make deploy. Local testing is possible with python server-enhanced.py after installing dependencies. Integrate with Claude Desktop by copying the claude-desktop-config.json to the appropriate configuration path.

Key features of MCP Server on EKS

  • Real Kubernetes API integration with actual EKS clusters
  • Live cluster information with namespace counts and node status
  • Tools: get_cluster_status, list_pods, get_deployments
  • Health checks with liveness and readiness probes
  • Supports both in-cluster and local kubectl configurations
  • Claude Desktop and programmatic Python client integration

Use cases of MCP Server on EKS

  • Monitor real-time EKS cluster status and node health via Claude Desktop
  • List and inspect pods across any namespace with live status
  • Check deployment status and replica counts using natural language
  • Automate Kubernetes operations through a programmatic Python client
  • Test and validate MCP protocol implementations in a cloud-native environment

FAQ from MCP Server on EKS

What are the prerequisites?

AWS CLI configured, Docker installed, Terraform >= 1.0, kubectl installed, and an AWS account with an existing ECR repository.

How do I deploy with Karpenter?

Run ./scripts/deploy.sh all for a single-command deployment, or deploy in phases: ./scripts/deploy.sh infrastructure, then ./scripts/deploy.sh karpenter, then ./scripts/deploy.sh app.

How can I monitor the MCP server?

Use kubectl get service mcp-server-service -n mcp-server to access the service and kubectl logs -f deployment/mcp-server -n mcp-server to view logs.

How do I clean up resources?

For Karpenter deployments, remove resources in reverse order: kubectl delete -f k8s/, then destroy Karpenter Terraform, then the main Terraform. For legacy deployments, use make destroy (not explicitly shown but implied by cleanup section).

What does Karpenter add to the deployment?

Karpenter provides Kubernetes-native node autoscaling with a NodePool and EC2NodeClass, configuring instance types (t3.medium/large/xlarge), capacity types (spot/on-demand), and cost optimization by preferring spot instances.

Frequently asked questions

What are the prerequisites?

AWS CLI configured, Docker installed, Terraform >= 1.0, kubectl installed, and an AWS account with an existing ECR repository.

How do I deploy with Karpenter?

Run `./scripts/deploy.sh all` for a single-command deployment, or deploy in phases: `./scripts/deploy.sh infrastructure`, then `./scripts/deploy.sh karpenter`, then `./scripts/deploy.sh app`.

How can I monitor the MCP server?

Use `kubectl get service mcp-server-service -n mcp-server` to access the service and `kubectl logs -f deployment/mcp-server -n mcp-server` to view logs.

How do I clean up resources?

For Karpenter deployments, remove resources in reverse order: `kubectl delete -f k8s/`, then destroy Karpenter Terraform, then the main Terraform. For legacy deployments, use `make destroy` (not explicitly shown but implied by cleanup section).

What does Karpenter add to the deployment?

Karpenter provides Kubernetes-native node autoscaling with a NodePool and EC2NodeClass, configuring instance types (t3.medium/large/xlarge), capacity types (spot/on-demand), and cost optimization by preferring spot instances.

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