🎯 Kubernetes AI Management System

AI-Powered Kubernetes Management (MCP + Agent)

    ⎈ K8s AI Management
    ├── 🤖 MCP Server
    ├── 🔍 K8s Tools
    └── 🚀 Agent mode with Rest API

✨ Overview

This project combines the power of AI with Kubernetes management. Users can perform real-time diagnostics, resource monitoring, and smart log analysis. It simplifies Kubernetes management through conversational AI, providing a modern alternative.

💡 Just ask questions naturally - no need to memorize commands!

🏗️ Project Structure

The project is organized into the following modules:

agent: Agent mode backed by Rest API to analyze the cluster using natural language
mcp-server: MCP server backed by tools which can be integrated with MCP host (like Claude desktop) to provide a full experience
tools: Kubernetes tools for cluster analysis/management (used by both agent and mcp-server)

🎁 Features

This AI-powered system understands natural language queries about your Kubernetes cluster. Here are some of the capabilities provided by the system which can be queried using natural language:

🏥 Cluster Health and Diagnostics

"What's the status of my cluster?"
"Show me all pods in the default namespace"
"Are there any failing pods? in default namespace"
"What's using the most resources in my cluster?"
"Give me a complete health check of the cluster"
"Are there any nodes not in Ready state?"
"Show me pods in default namespace that have been running for more than 7 days"
"Identify any pods running in default namespace with high restart counts"

🌐 Network Analysis

"Show me the logs for the payment service"
"List all ingresses in the cluster"
"Show me all services and their endpoints"
"Check if my service 'api-gateway' has any endpoints"
"Show me all exposed services with external IPs"

💾 Storage Management

"List all persistent volumes in the cluster"
"Show me storage claims that are unbound"
"What storage classes are available in the cluster?"
"Which pods are using persistent storage?"
"Are there any storage volumes nearing capacity?"

⏱️ Job and CronJob Analysis

"List all running jobs in the batch namespace"
"Show me failed jobs from the last 24 hours"
"What CronJobs are scheduled to run in the next hour?"
"Show me the execution history of the 'backup' job"

⎈ Helm Release Management

"List all Helm releases"
"Upgrade the MongoDB chart to version 12.1.0"
"What values are configured for my Prometheus release?"
"Rollback the failed Elasticsearch release"
"Show me the revision history for my Prometheus release"
"Compare values between different Helm releases"
"Check for outdated Helm charts in my cluster"
"What are the dependencies for my Elasticsearch chart?"

Note: The system uses AI to analyze patterns in logs, events, and resource usage to provide intelligent diagnostics and recommendations.

🛠️ Prerequisites

Requirement	Version
☕ JDK	17 or later
🧰 Maven	3.8 or later
⎈ Minikube/Any Kubernetes cluster	Configured `~/.kube/config`

Note: The system uses the kubeconfig file from ~/.kube/config, so make sure it is properly configured.

🏗️ 1. Project Build

# Build all modules
mvn clean package

# Run the MCP server
java -jar mcp-server/target/mcp-server-1.0-SNAPSHOT.jar

# Alternatively, run the agent directly
java -jar agent/target/agent-*-fat.jar

🛠️ 2. Minikube setup

Install minikube and create a nginx deployment:

# Install minikube
brew install minikube

# Start minikube
minikube start

# Make sure kubeconfig is set
kubectl config use-context minikube

# Deploy nginx
kubectl create deployment nginx --image=nginx:latest

# Check whether nginx is running
kubectl get pods

Note: You should see nginx pod in the output

🛠️ 3. Testing project

🤝 3.1 MCP Server integration with Claude Desktop

Refer to mcp-server/README.md for instructions on how to integrate with Claude Desktop

3.2. Agent Mode with Rest API

Refer to agent/README.md for instructions on how to run the agent

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.