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mcp-kubernetes

by Azure·57·Score 46

An MCP server enabling AI assistants to interact with Kubernetes clusters through kubectl commands.

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Overview

mcp-kubernetes is a comprehensive Model Context Protocol server developed by Azure that bridges AI assistants with Kubernetes environments. It provides secure access levels (readonly, readwrite, admin) and supports both a unified kubectl tool and specialized tools for different Kubernetes operations. The server allows AI tools to query resources, execute commands, manage clusters, and diagnose Kubernetes states through natural language interactions, making it particularly valuable for DevOps teams using AI assistants.

Try asking AI

After installing, here are 5 things you can ask your AI assistant:

you:Kubernetes cluster administration through natural language prompts in AI assistants
you:Automated troubleshooting of pod and deployment issues using AI
you:Scaling and managing Kubernetes resources via conversational AI interfaces
you:What access levels does mcp-kubernetes support?
you:How can I switch between unified and legacy tools?

When to choose this

Choose this for AI assistants that need to interact with Kubernetes clusters, especially when you want granular control over access levels and a unified tool interface to minimize context usage.

When NOT to choose this

Don't use this if you need to integrate with other cloud providers beyond Kubernetes, or if your AI tools require direct API access to Kubernetes rather than kubectl command translation.

Tools this server exposes

10 tools extracted from the README
  • call_kubectl

    Execute kubectl commands to interact with Kubernetes clusters

  • kubectl_resources

    Manage Kubernetes resources (CRUD operations and node management)

  • kubectl_workloads

    Manage workload deployments including scaling and rollouts

  • kubectl_metadata

    Update labels, annotations, and other metadata on resources

  • kubectl_diagnostics

    Debug and monitor Kubernetes resources

  • kubectl_cluster

    View cluster information and API discovery

  • kubectl_config

    Configuration and security operations including kubectl context management

  • call_helm

    Run Helm commands for managing Kubernetes applications

  • call_cilium

    Run Cilium commands for network policies and observability

  • call_hubble

    Run Hubble commands for network monitoring and debugging

Comparable tools

k-mcpkubectl-shellkube-mcp

Installation

Installation Options

Docker
{
  "mcpServers": {
    "kubernetes": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--mount",
        "type=bind,src=/home/username/.kube/config,dst=/home/mcp/.kube/config",
        "ghcr.io/azure/mcp-kubernetes"
      ]
    }
  }
}
Local Installation
  1. Install kubectl: https://kubernetes.io/docs/tasks/tools/
  2. Install helm: https://helm.sh/docs/intro/install/
  3. Configure your MCP server:
{
  "mcpServers": {
    "kubernetes": {
      "command": "<path of binary 'mcp-kubernetes'>",
      "args": ["--transport", "stdio"],
      "env": {
        "KUBECONFIG": "<your-kubeconfig-path>"
      }
    }
  }
}

FAQ

What access levels does mcp-kubernetes support?
The server supports three access levels: 'readonly' (default) for read-only operations, 'readwrite' for read and write operations, and 'admin' for all operations including admin commands like cordon, drain, and taint.
How can I switch between unified and legacy tools?
By default, mcp-kubernetes uses a unified 'call_kubectl' tool. To use legacy mode with multiple specialized tools, set the environment variable USE_LEGACY_TOOLS to 'true'.

Compare mcp-kubernetes with

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Last updated · Auto-generated from public README + GitHub signals.