k8s-mcp

# k8s-mcp

AI-native Kubernetes operations for agents and fast-moving teams.

k8s-mcp is a lightweight [MCP](https://modelcontextprotocol.io/) server that lets AI assistants deploy, inspect, and operate Kubernetes workloads through high-level workflow tools instead of raw kubectl commands — with structured outputs that significantly reduce token usage.

Works with **Claude Code** · **Codex CLI** · **Gemini CLI** · **Opencode** · and any MCP-compatible agent.

*Less kubectl. More done.*

## Why This Exists

AI assistants today can suggest `kubectl` commands. But actually operating a cluster still means switching contexts, copy-pasting commands, manually debugging failures, and repeatedly checking logs and events. That creates a slow human-in-the-loop cycle.

k8s-mcp closes this gap by giving AI agents **task-complete tools** instead of low-level primitives. Instead of chaining:

```bash
kubectl get pod
kubectl describe pod
kubectl logs
```

agents can call tools like `diagnose_pod()` or `wait_for_ready()` — and get structured results in one shot.

### What makes it different

k8s-mcp is designed around **workflows**, not raw resource access — optimized for:

- **AI agent execution loops** — deploy, observe, diagnose, retry, validate
- **Lower token usage** — structured outputs instead of long shell transcripts
- **Beginner-friendly operations** — less manual command chaining
- **Faster iteration** — fewer moving pieces between "please deploy this" and a working result

### Key capabilities

- **Faster and lighter than shell tools** — native MCP tools return structured data directly to the AI, avoiding shell spawning, CLI output parsing, and large text streaming
- **Diagnose issues in one shot** — `diagnose_pod` combines status, conditions, events, and failing-container logs into a single report
- **Autonomous deploy loops** — apply manifests, wait for readiness, detect failures, and iterate without manual back-and-forth
- **Generate deployment manifests** — create a