MCP Server Kubernetes

MCP Server that can connect to a Kubernetes cluster and manage it. Supports loading kubeconfig from multiple sources in priority order.

https://github.com/user-attachments/assets/f25f8f4e-4d04-479b-9ae0-5dac452dd2ed

Installation & Usage

Prerequisites

Before using this MCP server with any tool, make sure you have:

1. kubectl installed and in your PATH
2. A valid kubeconfig file with contexts configured
3. Access to a Kubernetes cluster configured for kubectl (e.g. minikube, Rancher Desktop, GKE, etc.)
4. Helm v3 installed and in your PATH (no Tiller required). Optional if you don't plan to use Helm.

You can verify your connection by running kubectl get pods in a terminal to ensure you can connect to your cluster without credential issues.

By default, the server loads kubeconfig from ~/.kube/config. For additional authentication options (environment variables, custom paths, etc.), see ADVANCED_README.md.

Claude Code

Add the MCP server to Claude Code using the built-in command:

claude mcp add kubernetes -- npx mcp-server-kubernetes

This will automatically configure the server in your Claude Code MCP settings.

Codex

Add the MCP server to Codex CLI using the built-in command:

codex mcp add kubernetes -- npx mcp-server-kubernetes

This registers the server globally in ~/.codex/config.toml and makes its tools available in all Codex sessions.

Claude Desktop

Add the following configuration to your Claude Desktop config file:

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

Claude Desktop Connector via mcpb

MCP Server Kubernetes is also available as a mcpb (formerly dxt) extension. In Claude Desktop, go to Settings (Cmd+, on Mac) -> Extensions -> Browse Extensions and scroll to find mcp-server-kubernetes in the modal. Install it & it will install & utilize kubectl via command line & your kubeconfig.

To manually install, you can also get the .mcpb by going to the latest Release and downloading it.

VS Code

For VS Code integration, you can use the MCP server with extensions that support the Model Context Protocol:

1. Install a compatible MCP extension (such as Claude Dev or similar MCP clients)
2. Configure the extension to use this server:

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["mcp-server-kubernetes"],
      "description": "Kubernetes cluster management and operations"
    }
  }
}

Cursor

Cursor supports MCP servers through its AI integration. Add the server to your Cursor MCP configuration:

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

The server will automatically connect to your current kubectl context. You can verify the connection by asking the AI assistant to list your pods or create a test deployment.

Usage with mcp-chat

mcp-chat is a CLI chat client for MCP servers. You can use it to interact with the Kubernetes server.

npx mcp-chat --server "npx mcp-server-kubernetes"

Alternatively, pass it your existing Claude Desktop configuration file from above (Linux should pass the correct path to config):

Mac:

npx mcp-chat --config "~/Library/Application Support/Claude/claude_desktop_config.json"

Windows:

npx mcp-chat --config "%APPDATA%\Claude\claude_desktop_config.json"

Features

• Connect to a Kubernetes cluster
• Unified kubectl API for managing resources
  • Get or list resources with kubectl_get
  • Describe resources with kubectl_describe
  • List resources with kubectl_get
  • Create resources with kubectl_create
  • Apply YAML manifests with kubectl_apply
  • Delete resources with kubectl_delete
  • Get logs with kubectl_logs
  • Manage kubectl contexts with kubectl_context
  • Explain Kubernetes resources with explain_resource
  • List API resources with list_api_resources
  • Scale resources with kubectl_scale
  • Update field(s) of a resource with kubectl_patch
  • Manage deployment rollouts with kubectl_rollout
  • Execute any kubectl command with kubectl_generic
  • Verify connection with ping
• Advanced operations
  • Scale deployments with kubectl_scale (replaces legacy scale_deployment)
  • Port forward to pods and services with port_forward
  • Run Helm operations
    • Install, upgrade, and uninstall charts
    • Support for custom values, repositories, and versions
    • Template-based installation (helm_template_apply) to bypass authentication issues
    • Template-based uninstallation (helm_template_uninstall) to bypass authentication issues
  • Pod cleanup operations
    • Clean up problematic pods (cleanup_pods) in states: Evicted, ContainerStatusUnknown, Completed, Error, ImagePullBackOff, CrashLoopBackOff
  • Node management operations
    • Cordoning, draining, and uncordoning nodes (node_management) for maintenance and scaling operations
• Troubleshooting Prompt (k8s-diagnose)
  • Guides through a systematic Kubernetes troubleshooting flow for pods based on a keyword and optional namespace.
• Non-destructive mode for read and create/update-only access to clusters
• Secrets masking for security (masks sensitive data in kubectl get secrets commands, does not affect logs)
• OpenTelemetry Observability (opt-in)
  • Distributed tracing for all tool calls
  • Export to Jaeger, Tempo, Grafana, or any OTLP backend
  • Configurable sampling strategies
  • Rich span attributes (tool name, duration, K8s context, errors)
  • See docs/OBSERVABILITY.md for details

Observability

The MCP Kubernetes server includes optional OpenTelemetry integration for comprehensive observability. This feature is disabled by default and can be enabled via environment variables or Helm configuration.

Quick Start

Enable observability with environment variables:

export ENABLE_TELEMETRY=true
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317

npx mcp-server-kubernetes

What Gets Traced

• All tool calls: kubectl_get, kubectl_apply, kubectl_logs, etc.
• Execution duration: How long each operation takes
• Success/failure status: Automatic error tracking
• Kubernetes context: Namespace, context, resource type
• Rich metadata: Host, process, and custom attributes

Backends Supported

Works with any OTLP-compatible backend:

• Jaeger (open source)
• Grafana Tempo (open source)
• Grafana Cloud (commercial)
• Datadog, New Relic, Honeycomb, Lightstep, AWS X-Ray

Configuration

See docs/OBSERVABILITY.md for comprehensive documentation including:

• Configuration options
• Deployment examples (Kubernetes, Helm, Claude Code)
• Sampling strategies
• Production best practices
• Troubleshooting guide

Example with Jaeger

# Start Jaeger
docker run -d --name jaeger \
  -e COLLECTOR_OTLP_ENABLED=true \
  -p 16686:16686 \
  -p 4317:4317 \
  jaegertracing/all-in-one:latest

# Enable telemetry
export ENABLE_TELEMETRY=true
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
export OTEL_TRACES_SAMPLER=always_on

# Run server
npx mcp-server-kubernetes

# View traces: http://localhost:16686

Prompts

The MCP Kubernetes server includes specialized prompts to assist with common diagnostic operations.

/k8s-diagnose Prompt

This prompt provides a systematic troubleshooting flow for Kubernetes pods. It accepts a keyword to identify relevant pods and an optional namespace to narrow the search. The prompt's output will guide you through an autonomous troubleshooting flow, providing instructions for identifying issues, collecting evidence, and suggesting remediation steps.

Local Development

Make sure that you have bun installed. Clone the repo & install dependencies:

git clone https://github.com/Flux159/mcp-server-kubernetes.git
cd mcp-server-kubernetes
bun install

Development Workflow