mcp-kubernetes-server

The mcp-kubernetes-server is a server implementing the Model Context Protocol (MCP) to enable AI assistants (such as Claude, Cursor, and GitHub Copilot) to interact with Kubernetes clusters. It acts as a bridge, translating natural language requests from these assistants into Kubernetes operations and returning the results.

It allows AI assistants to:

• Query Kubernetes resources
• Execute kubectl commands
• Manage Kubernetes clusters through natural language interactions
• Diagnose and interpret the states of Kubernetes resources

How It Works

The mcp-kubernetes-server acts as an intermediary between AI assistants (that support the Model Context Protocol) and your Kubernetes cluster. It receives natural language requests from these assistants, translates them into kubectl commands or direct Kubernetes API calls, and executes them against the target cluster. The server then processes the results and returns a structured response, enabling seamless interaction with your Kubernetes environment via the AI assistant.

How To Install

Prerequisites

Before installing mcp-kubernetes-server, ensure you have the following:

• A working Kubernetes cluster.
• A kubeconfig file correctly configured to access your Kubernetes cluster (the server requires this file for interaction).
• The kubectl command-line tool installed and in your system's PATH (used by the server to execute many Kubernetes commands).
• The helm command-line tool installed and in your system's PATH (used by the server for Helm chart operations).
• Python >= 3.11, if you plan to install and run the server directly using uvx (without Docker).

Docker

Get your kubeconfig file for your Kubernetes cluster and setup in the mcpServers (replace src path with your kubeconfig path):

{
  "mcpServers": {
    "kubernetes": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--mount", "type=bind,src=/home/username/.kube/config,dst=/home/mcp/.kube/config",
        "ghcr.io/feiskyer/mcp-kubernetes-server"
      ]
    }
  }
}

UVX

To run the server using uvx (a tool included with uv, the Python packager), first ensure uv is installed:

<details> <summary>Install uv</summary>

Install uv if it's not installed yet and add it to your PATH, e.g. using curl:

# For Linux and MacOS
curl -LsSf https://astral.sh/uv/install.sh | sh

</details> <details> <summary>Install kubectl</summary>

Install kubectl if it's not installed yet and add it to your PATH, e.g.

# For Linux
curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl"

# For MacOS
curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/darwin/arm64/kubectl"

</details> <details> <summary>Install helm</summary>

Install helm if it's not installed yet and add it to your PATH, e.g.

curl -sSL https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash

</details> <br/>

Config your MCP servers in Claude Desktop, Cursor, ChatGPT Copilot, Github Copilot and other supported AI clients, e.g.

{
  "mcpServers": {
    "kubernetes": {
      "command": "uvx",
      "args": [
        "mcp-kubernetes-server"
      ],
      "env": {
        "KUBECONFIG": "<your-kubeconfig-path>"
      }
    }
  }
}

MCP Server Options

<details> <summary>Environment Variables</summary>

Environment variables:

• KUBECONFIG: Path to your kubeconfig file, e.g. /home/<username>/.kube/config.

</details> <details> <summary>Command line arguments</summary>

Command-line Arguments:

usage: main.py [-h] [--disable-kubectl] [--disable-helm] [--disable-write]
               [--disable-delete] [--transport {stdio,sse,streamable-http}]
               [--host HOST] [--port PORT]

MCP Kubernetes Server

options:
  -h, --help            show this help message and exit
  --disable-kubectl     Disable kubectl command execution
  --disable-helm        Disable helm command execution
  --disable-write       Disable write operations
  --disable-delete      Disable delete operations
  --transport {stdio,sse,streamable-http}
                        Transport mechanism to use (stdio or sse or streamable-http)
  --host HOST           Host to use for sse or streamable-http server
  --port PORT           Port to use for sse or streamable-http server

</details>

Usage

Once the mcp-kubernetes-server is installed and configured in your AI client (using the JSON snippets provided in the 'How to install' section for Docker or UVX), you can start interacting with your Kubernetes cluster through natural language. For example, you can ask:

What is the status of my Kubernetes cluster?

What is wrong with my nginx pod?

Verifying the server: If you're running the server with stdio transport (common for uvx direct execution), the AI client will typically start and manage the server process. For sse or streamable-http transports, the server runs independently. You would have started it manually (e.g., uvx mcp-kubernetes-server --transport sse) and should see output in your terminal indicating it's running (e.g., INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)). You can also check for any error messages in the server terminal if the AI client fails to connect.

Available Tools

The mcp-kubernetes-server provides a comprehensive set of tools for interacting with Kubernetes clusters, categorized by operation type:

<details> <summary>Command Tools</summary>

Command Tools

These tools provide general command execution capabilities:

</details> <details> <summary>Read Tools</summary>

Read Tools

These tools provide read-only access to Kubernetes resources:

</details> <details> <summary>Write Tools</summary>

Write Tools

These tools provide create, update or patch operations to Kubernetes resources:

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