🔭 K8s Lens MCP

A Model Context Protocol (MCP) server for intelligent, natural-language-powered Kubernetes operations.

Stop memorizing kubectl flags. Ask your cluster questions in plain English.

✨ What It Does

K8s Lens MCP exposes deep analytical tools to any MCP-compatible AI assistant (Claude, Cursor, Copilot, etc.):

Unlike thin kubectl wrappers, K8s Lens MCP analyzes and correlates data so the AI can give you real answers, not just raw command output.

🚀 Quick Start

1. Install

pip install k8s-lens-mcp

Or with Poetry:

poetry add k8s-lens-mcp

2. Configure Your AI Client

#### Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "k8s-lens": {
      "command": "k8s-lens-mcp",
      "args": []
    }
  }
}

#### Cursor

Add to Cursor Settings → MCP:

{
  "mcpServers": {
    "k8s-lens": {
      "command": "k8s-lens-mcp",
      "args": []
    }
  }
}

3. Start Talking to Your Cluster

Open Claude or Cursor and ask:

"Show me all failing pods in the default namespace and tell me why they're failing."

🛡️ Safety First

• Read-only by default — the server starts in --read-only mode. No accidental deletions.
• RBAC-respecting — we use your kubeconfig / ServiceAccount. We don't bypass Kubernetes permissions.
• Secret masking — Kubernetes Secret data is never returned in tool output.

To enable read-write mode (future feature):

k8s-lens-mcp --read-only=false

🏗️ Development

Prerequisites

• Python 3.11+
• Poetry
• A Kubernetes cluster (e.g. kind, minikube, or a remote cluster)

Setup

git clone https://github.com/yourusername/k8s-lens-mcp.git
cd k8s-lens-mcp
poetry install

Run Locally

poetry run k8s-lens-mcp

Test with MCP Inspector

npx @modelcontextprotocol/inspector poetry run k8s-lens-mcp

Run Tests

poetry run pytest

Lint & Format

poetry run ruff check .
poetry run ruff format .

📋 Roadmap

• [x] Core MCP server scaffold
• [x] get_resources — filtered resource queries
• [x] analyze_pod — multi-signal root cause analysis
• [x] compare_deployments — cross-environment diffing
• [x] generate_manifest — best-practice manifest generation
• [ ] Cost / resource optimization advisor
• [ ] Multi-cluster context aggregation
• [ ] Helm release values diffing
• [ ] In-cluster deployment (Helm chart)
• [ ] kubectl plugin wrapper (kubectl lens "...")

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Please read our Code of Conduct.

📄 License

MIT