gke-mcp

Systematically diagnose GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads. Identifies preemption events, maintenance interruptions, bad host VMs, unhealthy pods, and coordinator worker failures.

Diagnose and predict node disruption during Compute Engine host maintenance for GPU and TPU workloads.

Diagnose and prevent `vbar_control_agent` segfaults and OOMs caused by race conditions during TPU device resets and frequent metrics collection (e.g. every 3s). Use when TPU slice initialization fails or `vbar_control_agent` crashes on TPU v6e nodes.

Verifies if a GKE or Kubernetes cluster is unused (no active compute, external exposure, or persistent data) before allowing deletion. Evaluates external exposure (LoadBalancer Service, Ingress, Gateway, MultiClusterIngress), persistent data (Bound PVC), and active compute (Running/Pending Pods in user namespaces) with low-overhead queries and fail-close timeouts.

Monitor and manage GKE TPU Dynamic Slices custom resources. Use when checking slice lifecycle states, troubleshooting failed slice creations (e.g. SliceCreationFailed, FAILED), running single or multi-slice workloads, or safely deleting/disabling slices.

Monitor and troubleshoot GKE TPU workloads using GKE system metrics and PromQL.

Dynamically generates specialized GKE skills for complex troubleshooting, operational workflows, architectural setup, or performance/cost optimization. Trigger this skill whenever the user faces a novel or non-obvious GKE challenge, needs custom cluster management workflows, or standard agent capabilities fall short, even if they don't explicitly ask to create a skill.

Expert instructions for building high-quality GKE troubleshooting skills. Codifies Step 0 context rules, zero-hallucination signatures, and explicit LQL/PromQL query requirements.

Assists in preparing applications and clusters on GKE for production.

Workflows for containerizing and deploying applications to GKE for the first time.

Workflows for auditing and hardening the security of GKE workloads.

Answer natural language questions about GKE-related costs by leveraging BigQuery export and cost allocation data.

Guides the user through creating GKE clusters using pre-defined templates (Standard, Autopilot, GPU/AI).

Guidance on managing the lifecycle and upgrades of Google Kubernetes Engine (GKE) clusters.

Guidance on optimizing costs for Google Kubernetes Engine (GKE) clusters.

Guidance on implementing multi-tenancy and governance in Google Kubernetes Engine (GKE) clusters.

Guidance on managing storage in Google Kubernetes Engine (GKE) clusters.

Workflows for configuring Backup for GKE and disaster recovery.

Workflows for configuring edge networking, ingress, and security on GKE.

Workflows for setting up and auditing observability (logging, monitoring, tracing) on GKE.

Workflows for ensuring high availability and reliability of GKE workloads.

Specific workflows for scaling GKE workloads using HPA and VPA, as well as best practices for autoscaling configuration.

Expert at discovering golden base images for GKE custom nodes using technical specs or context clues.

Guide for creating GKE ComputeClass resources. Use this skill when users want to define custom node configurations, autoscaling priorities, or hardware requirements (e.g., Spot VMs, GPUs, specific machine families) for their GKE workloads.

Deploy optimized AI/ML inference workloads on GKE using Google's Inference Quickstart (GIQ). Covers model discovery, manifest generation, and deployment using native MCP tools and CLI.