Workflow-oriented Kubernetes MCP for AI agents. Deploy, diagnose, and operate clusters with less manual kubectl.

k8s-mcp

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

k8s-mcp is a lightweight MCP 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:

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