Multi Cluster Kubernetes MCP Server

@razvanmacovei

リポジトリ

基本情報

カテゴリ

クラウドとインフラ

ライセンス

Apache-2.0

トランスポート

stdio

作者

razvanmacovei

投稿者

Razvan Macovei

Multi Cluster Kubernetes MCP Server について

An MCP (Model Context Protocol) server application for Kubernetes operations, providing a standardized API to interact with multiple Kubernetes clusters simultaneously using multiple kubeconfig files.

#kubernetes #k8s#multicluster

Multi Cluster Kubernetes MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "kubernetes": {
      "command": "uv",
      "args": [
        "--directory",
        "/servers/k8s-multicluster-mcp",
        "run",
        "app.py"
      ],
      "env": {
        "KUBECONFIG_DIR": "/kubeconfigs"
      }
    }
  }
}

Multi Cluster Kubernetes MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Multi Cluster Kubernetes MCP Server?

An MCP (Model Context Protocol) server for managing multiple Kubernetes clusters simultaneously. It provides 60+ tools covering cluster operations, resource management, monitoring, RBAC, storage, networking, and more, accessible through AI assistants like Claude Desktop. Designed for DevOps engineers and platform teams managing multiple Kubernetes clusters.

How to use Multi Cluster Kubernetes MCP Server?

Install via Smithery with npx -y @smithery/cli install @razvanmacovei/k8s-multicluster-mcp --client claude, or clone the repository and run python3 app.py. Configure the KUBECONFIG_DIR environment variable to point to a directory with kubeconfig files; the server automatically discovers all contexts. Required: Python 3.11+, valid kubeconfig files, and metrics-server for monitoring tools.

Key features of Multi Cluster Kubernetes MCP Server

Automatic discovery of all Kubernetes contexts across kubeconfig files.
Partial name matching for cluster selection (e.g., prod).
60+ tools for cluster health, resource CRUD, scaling, rollouts, RBAC, storage, networking.
Cross-cluster operations: compare resources, health, and configurations.
Context caching with 30-second TTL for performance.

Use cases of Multi Cluster Kubernetes MCP Server

Get a health overview of a production cluster via an AI assistant.
Compare pod counts in the backend namespace between prod and staging.
Diagnose application issues in a deployment with automated recommendations.
Scale a deployment to a specific number of replicas.
Manage secrets, ConfigMaps, and RBAC across multiple clusters.

よくある質問

What is the difference between this server and using kubectl directly?

This server provides an MCP interface for AI assistants to manage multiple clusters simultaneously, with automatic context discovery and cross-cluster comparison capabilities, unlike direct kubectl usage.

What are the runtime requirements?

Python 3.11 or higher, one or more kubeconfig files with valid cluster credentials, and `metrics-server` installed in clusters for `k8s_top_*` and `k8s_cluster_info` tools.

Where are kubeconfig files located?

By default, the server reads from `~/.kube`. You can set the `KUBECONFIG_DIR` environment variable to any directory containing kubeconfig files.

Are there any known limitations?

Namespace deletion protects system namespaces. The server requires `metrics-server` for monitoring tools. Pod exec supports quoted arguments fixed in v2.0.0.

What transport or authentication does it use?

The server uses the MCP protocol over stdio (local execution). Authentication relies on the Kubernetes credentials provided in the kubeconfig files.

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