rohitghumare64/kubectl-mcp-server repository overview

kubectl-mcp-server

Control your entire Kubernetes infrastructure through natural language conversations with AI.
Talk to your clusters like you talk to a DevOps expert. Debug crashed pods, optimize costs, deploy applications, audit security, manage Helm charts, and visualize dashboards, all through natural language.
Part of CNCF Landscape, Next to Terraform

⁠

⁠Installation

⁠Quick Start with npx (Recommended - Zero Install)

# Run directly without installation - works instantly!
npx -y kubectl-mcp-server

# Or install globally for faster startup
npm install -g kubectl-mcp-server

⁠Or install with pip (Python)

# Standard installation
pip install kubectl-mcp-server

# With interactive UI dashboards (recommended)
pip install kubectl-mcp-server[ui]

⁠📑 Table of Contents

• What Can You Do?⁠
• Why kubectl-mcp-server?⁠
• Live Demos⁠
• Installation⁠
  • Quick Start with npx⁠
  • Install with pip⁠
  • Docker⁠
• Getting Started⁠
• Quick Setup with Your AI Assistant⁠
• All Supported AI Assistants⁠
• Complete Feature Set⁠
• Using the CLI⁠
• Advanced Configuration⁠
• Optional Features⁠
  • Interactive Dashboards⁠
  • Browser Automation⁠
• Enterprise⁠
• Integrations & Ecosystem⁠
• In-Cluster Deployment⁠
• Multi-Cluster Support⁠
• Architecture⁠
• Agent Skills⁠
• Development & Testing⁠
• Contributing⁠
• Support & Community⁠

⁠What Can You Do?

Simply ask your AI assistant in natural language:

💬 "Why is my pod crashing?"

• Instant crash diagnosis with logs, events, and resource analysis
• Root cause identification with actionable recommendations

💬 "Deploy a Redis cluster with 3 replicas"

• Creates deployment with best practices
• Configures services, persistent storage, and health checks

💬 "Show me which pods are wasting resources"

• AI-powered cost optimization analysis
• Resource recommendations with potential savings

💬 "Which services can't reach the database?"

• Network connectivity diagnostics with DNS resolution
• Service chain tracing from ingress to pods

💬 "Audit security across all namespaces"

• RBAC permission analysis
• Secret security scanning and pod security policies

💬 "Show me the cluster dashboard"

• Interactive HTML dashboards with live metrics
• Visual timeline of events and resource usage

253 powerful tools | 8 workflow prompts | 8 data resources | Works with all major AI assistants

⁠Why kubectl-mcp-server?

• 🚀 Stop context-switching - Manage Kubernetes directly from your AI assistant conversations
• 🧠 AI-powered diagnostics - Get intelligent troubleshooting, not just raw data
• 💰 Built-in cost optimization - Identify waste and get actionable savings recommendations
• 🔒 Enterprise-ready - OAuth 2.1 auth, RBAC validation, non-destructive mode, secret masking
• ⚡ Zero learning curve - Natural language instead of memorizing kubectl commands
• 🌐 Universal compatibility - Works with Claude, Cursor, Windsurf, Copilot, and 15+ other AI tools
• 📊 Visual insights - Interactive dashboards and browser automation for web-based tools
• ☸️ Production-grade - Deploy in-cluster with kMCP, 216 passing tests, active maintenance

From debugging crashed pods to optimizing cluster costs, kubectl-mcp-server is your AI-powered DevOps companion.

⁠Live Demos

⁠Claude Desktop

⁠Cursor AI

⁠Windsurf

⁠Installation

⁠Quick Start with npx (Recommended - Zero Install)

# Run directly without installation - works instantly!
npx -y kubectl-mcp-server

# Or install globally for faster startup
npm install -g kubectl-mcp-server

⁠Or install with pip (Python)

# Standard installation
pip install kubectl-mcp-server

# With interactive UI dashboards (recommended)
pip install kubectl-mcp-server[ui]

⁠Install from GitHub Release

# Install specific version directly from GitHub release (replace {VERSION} with desired version)
pip install https://github.com/rohitg00/kubectl-mcp-server/releases/download/v{VERSION}/kubectl_mcp_server-{VERSION}-py3-none-any.whl

# Example: Install v1.19.0
pip install https://github.com/rohitg00/kubectl-mcp-server/releases/download/v1.19.0/kubectl_mcp_server-1.19.0-py3-none-any.whl

# Or install latest from git
pip install git+https://github.com/rohitg00/kubectl-mcp-server.git

⁠Prerequisites

• Python 3.9+ (for pip installation)
• Node.js 14+ (for npx installation)
• kubectl installed and configured
• Access to a Kubernetes cluster

⁠Docker

# Pull from Docker Hub
docker pull rohitghumare64/kubectl-mcp-server:latest

# Or pull from GitHub Container Registry
docker pull ghcr.io/rohitg00/kubectl-mcp-server:latest

# Run with stdio transport
docker run -i -v $HOME/.kube:/root/.kube:ro rohitghumare64/kubectl-mcp-server:latest

# Run with HTTP transport
docker run -p 8000:8000 -v $HOME/.kube:/root/.kube:ro rohitghumare64/kubectl-mcp-server:latest --transport sse

⁠Getting Started

⁠1. Test the Server (Optional)

Before integrating with your AI assistant, verify the installation:

# Check if kubectl is configured
kubectl cluster-info

# Test the MCP server directly
kubectl-mcp-server info

# List all available tools
kubectl-mcp-server tools

# Try calling a tool
kubectl-mcp-server call get_pods '{"namespace": "kube-system"}'

⁠2. Connect to Your AI Assistant

Choose your favorite AI assistant and add the configuration:

⁠Quick Setup with Your AI Assistant

⁠Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["-y", "kubectl-mcp-server"]
    }
  }
}

⁠Cursor AI

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["-y", "kubectl-mcp-server"]
    }
  }
}

⁠Windsurf

Add to ~/.config/windsurf/mcp.json:

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["-y", "kubectl-mcp-server"]
    }
  }
}

⁠Using Python Instead of npx

{
  "mcpServers": {
    "kubernetes": {
      "command": "python",
      "args": ["-m", "kubectl_mcp_tool.mcp_server"],
      "env": {
        "KUBECONFIG": "/path/to/.kube/config"
      }
    }
  }
}

More integrations: GitHub Copilot, Goose, Gemini CLI, Roo Code, and 15+ other clients⁠ —> see full configuration guide⁠ below.

⁠3. Restart Your AI Assistant

After adding the configuration, restart your AI assistant (GitHub Copilot, Claude Code,Claude Desktop, Cursor, etc.) to load the MCP server.

⁠4. Try These Commands

Start a conversation with your AI assistant and try these:

Troubleshooting:

"Show me all pods in the kube-system namespace"
"Why is the nginx-deployment pod crashing?"
"Diagnose network connectivity issues in the default namespace"

Deployments:

"Create a deployment for nginx with 3 replicas"
"Scale my frontend deployment to 5 replicas"
"Roll back the api-server deployment to the previous version"

Cost & Optimization:

"Which pods are using the most resources?"
"Show me idle resources that are wasting money"
"Analyze cost optimization opportunities in the production namespace"

Security:

"Audit RBAC permissions in all namespaces"
"Check for insecure secrets and configurations"
"Show me pods running with privileged access"

Helm:

"List all Helm releases in the cluster"
"Install Redis from the Bitnami chart repository"
"Show me the values for my nginx-ingress Helm release"

Multi-Cluster:

"List all available Kubernetes contexts"
"Switch to the production cluster context"
"Show me cluster information and version"

⁠MCP Client Compatibility

Works seamlessly with all MCP-compatible AI assistants:

⁠All Supported AI Assistants

⁠Claude Code

Add to ~/.config/claude-code/mcp.json:

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["-y", "kubectl-mcp-server"]
    }
  }
}

⁠GitHub Copilot (VS Code)

Add to VS Code settings.json:

{
  "mcp": {
    "servers": {
      "kubernetes": {
        "command": "npx",
        "args": ["-y", "kubectl-mcp-server"]
      }
    }
  }
}

⁠Goose

Add to ~/.config/goose/config.yaml:

extensions:
  kubernetes:
    command: npx
    args:
      - -y
      - kubectl-mcp-server

⁠Gemini CLI

Add to ~/.gemini/settings.json:

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["-y", "kubectl-mcp-server"]
    }
  }
}

⁠Roo Code / Kilo Code

Add to ~/.config/roo-code/mcp.json or ~/.config/kilo-code/mcp.json:

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["-y", "kubectl-mcp-server"]
    }
  }
}

⁠Complete Feature Set

⁠253 MCP Tools for Complete Kubernetes Management

⁠MCP Resources

Access Kubernetes data as browsable resources:

⁠MCP Prompts

Pre-built workflow prompts for common Kubernetes operations:

⁠Key Capabilities

• 🤖 253 Powerful Tools - Complete Kubernetes management from pods to security
• 🎯 8 AI Workflow Prompts - Pre-built workflows for common operations
• 📊 8 MCP Resources - Browsable Kubernetes data exposure
• 🎨 6 Interactive Dashboards - HTML UI tools for visual cluster management
• 🌐 26 Browser Tools - Web automation with cloud provider support
• 🔄 107 Ecosystem Tools - GitOps, Cert-Manager, Policy, Backup, KEDA, Cilium, Rollouts, CAPI, KubeVirt, Istio, vCluster
• ⚡ Multi-Transport - stdio, SSE, HTTP, streamable-http
• 🔐 Security First - Non-destructive mode, secret masking, RBAC validation
• 🏥 Advanced Diagnostics - AI-powered troubleshooting and cost optimization
• ☸️ Multi-Cluster - Target any cluster via context parameter in every tool
• 🎡 Full Helm v3 - Complete chart lifecycle management
• 🔧 Powerful CLI - Shell-friendly tool discovery and direct calling
• 🐳 Cloud Native - Deploy in-cluster with kMCP or kagent

⁠Using the CLI

The built-in CLI lets you explore and test tools without an AI assistant:

# List all tools with descriptions
kubectl-mcp-server tools -d

# Search for pod-related tools
kubectl-mcp-server grep "*pod*"

# Show specific tool schema
kubectl-mcp-server tools get_pods

# Call a tool directly
kubectl-mcp-server call get_pods '{"namespace": "kube-system"}'

# Pipe JSON from stdin
echo '{"namespace": "default"}' | kubectl-mcp-server call get_pods

# Check dependencies
kubectl-mcp-server doctor

# Show/switch Kubernetes context
kubectl-mcp-server context
kubectl-mcp-server context minikube

# List resources and prompts
kubectl-mcp-server resources
kubectl-mcp-server prompts

# Show server info
kubectl-mcp-server info

⁠CLI Features

• Structured errors: Actionable error messages with suggestions
• Colorized output: Human-readable with JSON mode for scripting (--json)
• NO_COLOR support: Respects NO_COLOR environment variable
• Stdin support: Pipe JSON arguments to commands

⁠Advanced Configuration

⁠Transport Modes

The server supports multiple transport protocols:

# stdio (default) - Best for Claude Desktop, Cursor, Windsurf
kubectl-mcp-server
# or: python -m kubectl_mcp_tool.mcp_server

# SSE - Server-Sent Events for web clients
kubectl-mcp-server --transport sse --port 8000

# HTTP - Standard HTTP for REST clients
kubectl-mcp-server --transport http --port 8000

# streamable-http - For agentgateway integration
kubectl-mcp-server --transport streamable-http --port 8000

Transport Options:

• --transport: Choose from stdio, sse, http, streamable-http (default: stdio)
• --host: Bind address (default: 0.0.0.0)
• --port: Port for network transports (default: 8000)
• --disable-destructive (or --non-destructive): Block destructive operations (allow create/update, block delete)
• --read-only: Block all write operations

⁠Environment Variables

Core Settings:

Authentication (Enterprise):

Browser Automation (Optional):

⁠Optional: Interactive Dashboards (6 UI Tools)

Get beautiful HTML dashboards for visual cluster management.

Installation:

# Install with UI support
pip install kubectl-mcp-server[ui]

6 Dashboard Tools:

• 📊 show_pods_dashboard_ui - Real-time pod status tab