KubeView MCP – Kubernetes Model Context Protocol Server

KubeView is a read-only Model Context Protocol (MCP) server that enables AI agents (like Cursor, Claude Desktop) to inspect, diagnose, and debug Kubernetes clusters safely. It provides a comprehensive set of tools for Kubernetes, Helm, Argo Workflows, and Argo CD.

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✨ Features

• 🛡️ Read-Only & Safe: Designed for production safety with zero write access and sensitive data masking.
• ☸️ Kubernetes Integration: List/get resources, fetch metrics, stream logs, execute commands, and diagnose network issues.
• 📦 Helm Support: Inspect releases, values, manifests, and history.
• 🐙 Argo Ecosystem: Manage Argo Workflows and Argo CD applications.
• 🧠 Code Mode: Sandboxed TypeScript environment for complex reasoning and multi-step workflows.

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🚀 Quick Start

Prerequisites

• Node.js ≥ 18
• Access to a Kubernetes cluster
• Optional CLIs on current $PATH if you want to use those plugins: helm, argo, argocd

Installation

# start the server
npx -y kubeview-mcp

# install as a claude code mcp server
claude mcp add kubernetes -- npx kubeview-mcp

Configuration for MCP Clients

Add to your mcpServers configuration (e.g., in Cursor or Claude Desktop):

{
  "mcpServers": {
    "kubeview": {
      "command": "npx",
      "args": ["-y", "kubeview-mcp"]
    }
  }
}

Environment Variables

Configure the server using environment variables:

Variable	Description	Default
KUBECONFIG	Path to kubeconfig file	~/.kube/config
MCP_MODE	Server mode: all, code, or tools	all
MCP_LOG_LEVEL	Log level (error, warn, info, debug)	info
MCP_HIDE_SENSITIVE	Enable global sensitive data masking	false

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🛠️ Tools Overview

Kubernetes

• kube_list: List resources or get cluster diagnostics.
• kube_get: Describe specific resources (supports all K8s types).
• kube_metrics: Fetch CPU/memory metrics for nodes and pods.
• kube_logs: Fetch or stream container logs.
• kube_exec: Execute commands in containers (read-only recommended).
• kube_port: Port-forward to pods/services.
• kube_net: Run in-cluster network diagnostics.

Helm

• helm_list: List Helm releases.
• helm_get: Fetch release values, manifests, and history.

Argo

• argo_list / argo_get: Manage Argo Workflows.
• argocd_app: Inspect Argo CD applications and resources.

Utilities

• run_code: Execute sandboxed TypeScript code for complex tasks.

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🧠 Code Mode

Inspired by Code execution with MCP, KubeView ships with a code-mode runtime that allows agents to explore the API, search tools, and execute complex workflows in a sandboxed environment.

What it provides

• MCP Bridge Layer: Seamlessly connects to all registered MCP server tools.
• Dynamic TypeScript Definitions: Automatically converts tool schemas into a strongly-typed global.d.ts resource, enabling agents to use valid TypeScript patterns and enjoy type safety without hallucinating parameters.
• Tool Search Utilities: Runtime helpers like tools.search() and tools.list() allow agents to progressively discover capabilities without needing to load the entire schema context upfront.
• Sandboxed Execution: A locked-down Node.js environment (via vm) with controlled access to the console and the tools global object, ensuring safe execution of agent-generated code.

Usage

For complex tasks requiring logic, loops, or data processing, use Code Mode:

"env": { "MCP_MODE": "code" }

💡 Pro Tip: Code Mode Prompt

The server includes a built-in prompt named code-mode that injects the full TypeScript API documentation, tool overview, and examples into the context.

In Cursor IDE: Simply type /kubeview/code-mode in the prompt (or select it from the / prompt menu). This gives the AI the exact context it needs to write correct run_code scripts immediately.

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💻 Local Development

1. Clone & Install:

   git clone https://github.com/mikhae1/kubeview-mcp.git
   cd kubeview-mcp
   npm install

2. Build & Run:

   npm run build
   npm start

3. Test:

   npm test

CLI Usage

You can test tools directly via the CLI:

npm run command -- kube_list --namespace=default

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📄 License

MIT © kubeview-mcp team