Give Your AI Assistant Native Kubernetes Powers

Stop wrapping kubectl commands in scripts and giving your AI assistant slow, brittle access to your clusters. This MCP server gives Claude, GitHub Copilot, and other AI tools direct, native access to the Kubernetes API - no external dependencies, no kubectl overhead.

Why This Beats kubectl Wrappers

Most Kubernetes MCP implementations are just kubectl command wrappers. Every operation means spawning a process, parsing text output, and dealing with inconsistent formatting. This server talks directly to the Kubernetes API using the same client-go library that powers kubectl itself.

Real performance difference: A kubectl wrapper takes 200-500ms per operation. Direct API calls? Sub-50ms. When your AI is debugging a failing deployment by checking pods, services, and events, that's the difference between a snappy conversation and waiting around.

What Your AI Can Actually Do

Full Resource Management

• CRUD operations on any Kubernetes resource (not just the common ones)
• Works with custom resources and CRDs out of the box
• Proper error handling with actual API server responses

Pod Operations That Work

• Stream logs in real-time during debugging sessions
• Execute commands inside containers for live troubleshooting
• Get actual resource usage metrics (CPU/memory) from the metrics server
• Run temporary pods for testing without leaving artifacts

Helm Integration

• Install, list, and uninstall Helm releases
• Pass complex values objects without YAML parsing nightmares
• Native Helm library integration (not helm CLI wrapping)

Cluster Intelligence

• Watch events across namespaces to understand what's happening
• Auto-detect configuration changes and cluster state
• Handle both vanilla Kubernetes and OpenShift projects

Real-World Use Cases

Incident Response Your AI can instantly pull logs from failing pods, check resource usage, examine recent events, and even exec into containers to run diagnostic commands - all in one conversation without you switching contexts.

Development Workflow "Deploy this containerized app to the dev namespace and expose it on port 8080" becomes a single AI interaction. The server handles pod creation, service configuration, and reports back the actual endpoint.

Configuration Debugging Instead of manually checking if your deployment has the right labels, resource limits, and environment variables, your AI can inspect the actual resource definitions and spot misconfigurations immediately.

Multi-Environment Management Switch between clusters and namespaces seamlessly. Your AI maintains context about which environment it's working with and can compare configurations across environments.

Zero-Dependency Setup

For Claude Desktop (fastest start):

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

For VS Code/GitHub Copilot: One-click installation via the extension marketplace or command line.

For Production: Native binaries for Linux, macOS, and Windows. No Node.js, Python, or container runtime required.

Built for Real Kubernetes Environments

Unlike hobby projects, this server handles:

• Multiple clusters and contexts
• RBAC and security constraints
• High-availability clusters with multiple API servers
• Both Kubernetes and OpenShift (including Projects)
• Custom resources and operators
• Read-only and restricted access modes for production environments

The server includes comprehensive safety features: read-only mode for production clusters, destructive operation controls, and proper error handling that doesn't leave your cluster in inconsistent states.

Integration Examples

Claude Desktop debugging session: "Check why my nginx deployment isn't starting" → AI examines the deployment, checks pod status, pulls container logs, identifies the configuration issue, and suggests the fix.

VS Code development flow: "Deploy this FastAPI app to my staging cluster" → AI creates the deployment manifest, applies it, creates a service, and reports back the access URL.

Incident response: "What's wrong with the payment service?" → AI checks all payment-related pods, examines recent events, pulls error logs, and identifies that the database connection is failing due to a ConfigMap change.

The server's native performance means these interactions happen in real-time, not after waiting for multiple kubectl commands to complete.

Ready to give your AI actual Kubernetes superpowers instead of slow CLI access? The setup takes under 2 minutes, and you'll immediately notice the difference in response time and capability.