kubectl

Installation

Quick Start with npx (Recommended - Zero Install)

bashCopy code
# 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)

bashCopy code
# 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)

bashCopy code
# 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)

bashCopy code
# Standard installation
pip install kubectl-mcp-server

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

Install from GitHub Release

bashCopy code
# 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

bashCopy code
# 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:

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# 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:

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

Cursor AI

Add to ~/.cursor/mcp.json:

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

Windsurf

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

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

Using Python Instead of npx

jsonCopy code
{
  "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:

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"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:

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"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:

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

Security:

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

Helm:

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"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:

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"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:

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

GitHub Copilot (VS Code)

Add to VS Code settings.json:

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

Goose

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

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

Gemini CLI

Add to ~/.gemini/settings.json:

jsonCopy code
{
  "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:

jsonCopy code
{
  "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:

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# 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:

bashCopy code
# 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:

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

6 Dashboard Tools:

• 📊 show_pods_dashboard_ui - Real-time pod status table
• 📝 show_pod_logs_ui - Interactive log viewer with search
• 🎯 show_cluster_overview_ui - Complete cluster dashboard
• ⚡ show_events_timeline_ui - Events timeline with filtering
• 📄 show_resource_yaml_ui - YAML viewer with syntax highlighting
• 📸 render_k8s_dashboard_screenshot - Export dashboards as PNG

Features:

• 🎨 Dark theme optimized for terminals (Catppuccin)
• 🔄 Graceful fallback to JSON for incompatible clients
• 🖼️ Screenshot rendering for universal compatibility
• 🚀 Zero external dependencies

Works With: Goose, LibreChat, Nanobot (full HTML UI) | Claude Desktop, Cursor, others (JSON + screenshots)

Optional: Browser Automation (26 Tools)

Automate web-based Kubernetes operations with agent-browser integration.

Quick Setup:

bashCopy code
# Install agent-browser
npm install -g agent-browser
agent-browser install

# Enable browser tools
export MCP_BROWSER_ENABLED=true
kubectl-mcp-server

What You Can Do:

• 🌐 Test deployed apps via Ingress URLs
• 📸 Screenshot Grafana, ArgoCD, or any K8s dashboard
• ☁️ Automate cloud console operations (EKS, GKE, AKS)
• 🏥 Health check web applications
• 📄 Export monitoring dashboards as PDF
• 🔐 Test authentication flows with persistent sessions

26 Available Tools: browser_open, browser_screenshot, browser_click, browser_fill, browser_test_ingress, browser_screenshot_grafana, browser_health_check, and 19 more

Advanced Features:

• Cloud providers: Browserbase, Browser Use
• Persistent browser profiles
• Remote CDP connections
• Session management

Optional: kubectl-mcp-app (8 Interactive UI Dashboards)

A standalone npm package that provides beautiful, interactive UI dashboards for Kubernetes management using the MCP ext-apps SDK.

Installation:

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# Via npm
npm install -g kubectl-mcp-app

# Or via npx (no install)
npx kubectl-mcp-app

Claude Desktop Configuration:

jsonCopy code
{
  "mcpServers": {
    "kubectl-app": {
      "command": "npx",
      "args": ["kubectl-mcp-app"]
    }
  }
}

8 Interactive UI Tools:

Features:

• 🎨 Dark/light theme support
• 📊 Real-time data visualization
• 🖱️ Interactive actions (scale, restart, delete)
• 🔗 Seamless integration with kubectl-mcp-server

More Info: See kubectl-mcp-app/README.md for full documentation.

Enterprise: OAuth 2.1 Authentication

Secure your MCP server with OAuth 2.1 authentication (RFC 9728).

bashCopy code
export MCP_AUTH_ENABLED=true
export MCP_AUTH_ISSUER=https://your-idp.example.com
export MCP_AUTH_AUDIENCE=kubectl-mcp-server
kubectl-mcp-server --transport http --port 8000

Supported Identity Providers: Okta, Auth0, Keycloak, Microsoft Entra ID, Google OAuth, and any OIDC-compliant provider.

Use Case: Multi-tenant environments, compliance requirements, audit logging.

Integrations & Ecosystem

Docker MCP Toolkit

Works with Docker MCP Toolkit:

bashCopy code
docker mcp server add kubectl-mcp-server mcp/kubectl-mcp-server:latest
docker mcp server configure kubectl-mcp-server --volume "$HOME/.kube:/root/.kube:ro"
docker mcp server enable kubectl-mcp-server
docker mcp client connect claude

agentregistry

Install from the centralized agentregistry:

bashCopy code
# Install arctl CLI
curl -fsSL https://raw.githubusercontent.com/agentregistry-dev/agentregistry/main/scripts/install.sh | bash

# Install kubectl-mcp-server
arctl mcp install io.github.rohitg00/kubectl-mcp-server

Available via: PyPI (uvx), npm (npx), OCI (docker.io/rohitghumare64/kubectl-mcp-server)

agentgateway

Route to multiple MCP servers through agentgateway:

bashCopy code
# Start with streamable-http
kubectl-mcp-server --transport streamable-http --port 8000

# Configure gateway
cat > gateway.yaml <<EOF
binds:
- port: 3000
  listeners:
  - routes:
    - backends:
      - mcp:
          targets:
          - name: kubectl-mcp-server
            mcp:
              host: http://localhost:8000/mcp
EOF

# Start gateway
agentgateway --config gateway.yaml

Connect clients to http://localhost:3000/mcp for unified access to all 253 tools.

In-Cluster Deployment

Option 1: kMCP (Recommended)

Deploy with kMCP - a control plane for MCP servers:

bashCopy code
# Install kMCP
curl -fsSL https://raw.githubusercontent.com/kagent-dev/kmcp/refs/heads/main/scripts/get-kmcp.sh | bash
kmcp install

# Deploy kubectl-mcp-server (easiest)
kmcp deploy package --deployment-name kubectl-mcp-server \
   --manager npx --args kubectl-mcp-server

# Or with Docker image
kmcp deploy --file deploy/kmcp/kmcp.yaml --image rohitghumare64/kubectl-mcp-server:latest

See kMCP quickstart for details.

Option 2: Standard Kubernetes

Deploy with kubectl/kustomize:

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# Using kustomize (recommended)
kubectl apply -k deploy/kubernetes/

# Or individual manifests
kubectl apply -f deploy/kubernetes/namespace.yaml
kubectl apply -f deploy/kubernetes/rbac.yaml
kubectl apply -f deploy/kubernetes/deployment.yaml
kubectl apply -f deploy/kubernetes/service.yaml

# Access via port-forward
kubectl port-forward -n kubectl-mcp svc/kubectl-mcp-server 8000:8000

See deploy/ directory for all manifests and configuration options.

Option 3: kagent (AI Agent Framework)

Integrate with kagent - a CNCF Kubernetes-native AI agent framework:

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# Install kagent
brew install kagent
kagent install --profile demo

# Register as ToolServer
kubectl apply -f deploy/kagent/toolserver-stdio.yaml

# Open dashboard
kagent dashboard

Your AI agents now have access to all 253 Kubernetes tools. See kagent quickstart.

Architecture

Copy code
┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│   AI Assistant  │────▶│   MCP Server     │────▶│  Kubernetes API │
│ (Claude/Cursor) │◀────│ (kubectl-mcp)    │◀────│    (kubectl)    │
└─────────────────┘     └──────────────────┘     └─────────────────┘

The MCP server implements the Model Context Protocol, translating natural language requests into kubectl operations.

Modular Structure

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kubectl_mcp_tool/
├── mcp_server.py          # Main server (FastMCP, transports)
├── tools/                  # 253 MCP tools organized by category
│   ├── pods.py            # Pod management & diagnostics
│   ├── deployments.py     # Deployments, StatefulSets, DaemonSets
│   ├── core.py            # Namespaces, ConfigMaps, Secrets
│   ├── cluster.py         # Context/cluster management
│   ├── networking.py      # Services, Ingress, NetworkPolicies
│   ├── storage.py         # PVCs, StorageClasses, PVs
│   ├── security.py        # RBAC, ServiceAccounts, PodSecurity
│   ├── helm.py            # Complete Helm v3 operations
│   ├── operations.py      # kubectl apply/patch/describe/etc
│   ├── diagnostics.py     # Metrics, namespace comparison
│   ├── cost.py            # Resource optimization & cost analysis
│   ├── ui.py              # MCP-UI interactive dashboards
│   ├── gitops.py          # GitOps (Flux/ArgoCD)
│   ├── certs.py           # Cert-Manager
│   ├── policy.py          # Policy (Kyverno/Gatekeeper)
│   ├── backup.py          # Backup (Velero)
│   ├── keda.py            # KEDA autoscaling
│   ├── cilium.py          # Cilium/Hubble network observability
│   ├── rollouts.py        # Argo Rollouts/Flagger
│   ├── capi.py            # Cluster API
│   ├── kubevirt.py        # KubeVirt VMs
│   ├── kiali.py           # Istio/Kiali service mesh
│   └── vind.py            # vCluster (virtual clusters)
├── resources/              # 8 MCP Resources for data exposure
├── prompts/                # 8 MCP Prompts for workflows
└── cli/                    # CLI interface

Agent Skills (25 Skills for AI Coding Agents)

Extend your AI coding agent with Kubernetes expertise using our Agent Skills library. Skills provide specialized knowledge and workflows that agents can load on demand.

Quick Install

bashCopy code
# Copy all skills to Claude
cp -r kubernetes-skills/claude/* ~/.claude/skills/

# Or install specific skills
cp -r kubernetes-skills/claude/k8s-helm ~/.claude/skills/

Available Skills (25)

Convert to Other Agents

Use SkillKit to convert skills to your preferred AI agent format:

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npm install -g skillkit

# Convert to Cursor format
skillkit translate kubernetes-skills/claude --to cursor --output .cursor/rules/

# Convert to Codex format
skillkit translate kubernetes-skills/claude --to codex --output ./

Supported agents: Claude, Cursor, Codex, Gemini CLI, GitHub Copilot, Goose, Windsurf, Roo, Amp, and more.

See kubernetes-skills/README.md for full documentation.

Multi-Cluster Support

Seamlessly manage multiple Kubernetes clusters through natural language. Every tool supports an optional context parameter to target any cluster without switching contexts.

Context Parameter (v1.15.0)

Most kubectl-backed tools accept an optional context parameter to target specific clusters. Note: vCluster (vind) and kind tools run via their local CLIs and do not accept the context parameter.

Talk to your AI assistant:

Copy code
"List pods in the production cluster"
"Get deployments from staging context"
"Show logs from the api-pod in the dev cluster"
"Compare namespaces between production and staging clusters"

Direct tool calls with context:

bashCopy code
# Target a specific cluster context
kubectl-mcp-server call get_pods '{"namespace": "default", "context": "production"}'

# Get deployments from staging
kubectl-mcp-server call get_deployments '{"namespace": "app", "context": "staging"}'

# Install Helm chart to production cluster
kubectl-mcp-server call install_helm_chart '{"name": "redis", "chart": "bitnami/redis", "namespace": "cache", "context": "production"}'

# Compare resources across clusters
kubectl-mcp-server call compare_namespaces '{"namespace1": "prod-ns", "namespace2": "staging-ns", "context": "production"}'

Context Management

Talk to your AI assistant:

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"List all available Kubernetes contexts"
"Switch to the production cluster"
"Show me details about the staging context"
"What's the current cluster I'm connected to?"

Or use the CLI directly:

bashCopy code
kubectl-mcp-server context                    # Show current context
kubectl-mcp-server context production         # Switch context
kubectl-mcp-server call list_contexts_tool    # List all contexts via MCP

How It Works

• If context is omitted, the tool uses your current kubectl context
• If context is specified, the tool targets that cluster directly
• Response includes "context": "production" or "context": "current" for clarity
• Works with all kubeconfig setups and respects KUBECONFIG environment variable
• No need to switch contexts for cross-cluster operations

Development & Testing

Setup Development Environment

bashCopy code
# Clone the repository
git clone https://github.com/rohitg00/kubectl-mcp-server.git
cd kubectl-mcp-server

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -r requirements-dev.txt

Running Tests

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# Run all tests
pytest tests/ -v

# Run specific test file
pytest tests/test_tools.py -v

# Run with coverage
pytest tests/ --cov=kubectl_mcp_tool --cov-report=html

# Run only unit tests
pytest tests/ -v -m unit

Test Structure

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tests/
├── __init__.py          # Test package
├── conftest.py          # Shared fixtures and mocks
├── test_tools.py        # Unit tests for 253 MCP tools
├── test_resources.py    # Tests for 8 MCP Resources
├── test_prompts.py      # Tests for 8 MCP Prompts
└── test_server.py       # Server initialization tests

234 tests covering: tool registration, resource exposure, prompt generation, server initialization, non-destructive mode, secret masking, error handling, transport methods, CLI commands, browser automation, and ecosystem tools.

Code Quality

bashCopy code
# Format code
black kubectl_mcp_tool tests

# Sort imports
isort kubectl_mcp_tool tests

# Lint
flake8 kubectl_mcp_tool tests

# Type checking
mypy kubectl_mcp_tool

Contributing

We ❤️ contributions! Whether it's bug reports, feature requests, documentation improvements, or code contributions.

Ways to contribute:

• 🐛 Report bugs via GitHub Issues
• 💡 Suggest features or improvements
• 📝 Improve documentation
• 🔧 Submit pull requests
• ⭐ Star the project if you find it useful!

Development setup: See Development & Testing section above.

Before submitting a PR:

1. Run tests: pytest tests/ -v
2. Format code: black kubectl_mcp_tool tests
3. Check linting: flake8 kubectl_mcp_tool tests

Support & Community

• 📖 Documentation
• 💬 GitHub Discussions
• 🐛 Issue Tracker
• 🎯 Feature Requests
• 🌟 agentregistry Profile

License

MIT License - see LICENSE for details.

Links & Resources

Package Repositories:

• 🐍 PyPI Package
• 📦 npm Package
• 🐳 Docker Hub

Project:

• 🔧 GitHub Repository
• 🐛 Issue Tracker
• 📋 Changelog

Ecosystem:

• 📚 Model Context Protocol
• ☸️ Kubernetes Documentation

Made with ❤️ for the Kubernetes and AI community

If kubectl-mcp-server makes your DevOps life easier, give it a ⭐ on GitHub!