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Multi Cluster Kubernetes MCP Server

@razvanmacovei

代码仓库

基本信息

分类

云与基础设施

许可证

Apache-2.0

传输方式

stdio

作者

razvanmacovei

提交者

Razvan Macovei

关于 Multi Cluster Kubernetes MCP Server

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 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Multi Cluster Kubernetes MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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.

常见问题

1

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.

2

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.

3

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.

4

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.

5

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.

评论

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