Kubernetes MCP
Kubernetes MCP server — manage clusters, pods, deployments, and services through AI-powered natural language commands.
// Try it now
"List all pods in my current namespace and show any that aren't in Running state"
- ! The server uses your kubeconfig context — make sure you're not accidentally pointing at production
- ! kubectl delete and scale operations are effective immediately — there's no 'are you sure' prompt from the cluster
- ! Pods in different namespaces need explicit namespace arguments or queries may look empty
API key or simple config
// When to use
- You're debugging a production or staging Kubernetes cluster issue
- You need to read pod logs and correlate them across services
- You want to scale deployments or restart pods through conversation
- You're learning Kubernetes and want AI to explain what's running
// When NOT to use
- You use Docker Compose, Nomad, or raw containers (use docker-mcp)
- You want to give AI write access to production clusters without review
- You need to edit cluster-level RBAC or admission controllers from AI
// Usage Scenarios
Pod Debugging
Investigate why a pod is failing by pulling logs and events.
Example prompt
"The api pod in namespace prod is crash-looping — get the last 100 lines of logs and describe why"
Deployment Scaling
Scale deployments up or down during traffic spikes or low-usage windows.
Example prompt
"Scale the checkout deployment to 5 replicas in the production namespace"
Cluster Overview
Get a natural-language summary of what's running across namespaces.
Example prompt
"List all deployments across namespaces and flag any that have pods not in Ready state"
// About
Plain English
Kubernetes is the system many companies use to run software in production. It groups applications into 'pods,' spreads them across machines, and restarts them if they crash. It's powerful, but it has a steep learning curve — the daily work means typing long kubectl commands and squinting at YAML files. This server lets your AI assistant do that work for you. Ask 'why is the checkout service crashing,' and Claude lists the broken pods, pulls the recent logs, and tells you what looks wrong. Ask 'scale the API up to five replicas because traffic is spiking,' and it makes the change. It can list services, inspect deployments, follow logs, and check the health of anything running in your cluster. For a developer who only occasionally touches Kubernetes, this turns a stressful tool into a conversation. For a team on call, it's a faster way to triage. The trade-off: actions are real and immediate. Use a kubeconfig pointed at a staging or development cluster while you're getting comfortable, and review what the AI is about to run before you approve it for production.
The Kubernetes MCP server provides AI assistants with access to your Kubernetes clusters. List pods, inspect deployments, view logs, manage services, apply manifests, and debug cluster issues — all through natural language.
A powerful tool for DevOps engineers and SREs who want to interact with Kubernetes without memorizing every kubectl command.
// Use Cases
- Inspect and debug Kubernetes pods through conversation
- Manage deployments and rollbacks with AI
- View and analyze cluster logs naturally
- Apply and validate Kubernetes manifests
// Works With
// Also Consider
// Related Servers
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