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Kubeopt MCP Server

@kubeopt

リポジトリ

基本情報

カテゴリ

その他

トランスポート

stdio

作者

kubeopt

投稿者

SrinivasK

Kubeopt MCP Server について

Kubernetes cluster cost analysis and optimization. Query costs, find savings opportunities, get rightsizing recommendations, and analyze pod spend across AKS, EKS, and GKE directly from Claude.

#kubernetes#devops#cost-optimization#cloud#finops#k8s#aws#azure#gcp

Kubeopt 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": {
    "kubeopt": {
      "command": "python3",
      "args": [
        "-m",
        "mcp_server.server"
      ],
      "env": {
        "KUBEOPT_API_URL": "http://localhost:5001",
        "KUBEOPT_USERNAME": "kubeopt",
        "KUBEOPT_PASSWORD": "your-password"
      }
    }
  }
}

Kubeopt 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 Kubeopt?

Kubeopt is the MCP server component of KubeOpt, a Kubernetes cost engineering tool. It exposes six tools over stdio transport, allowing AI assistants like Claude Desktop, Cursor, and Windsurf to query cluster cost data, optimization recommendations, and trigger fresh analyses from a running KubeOpt instance.

How to use Kubeopt?

Run KubeOpt locally (python main.py) or deploy it, then configure your MCP client to execute the server script (python -m mcp_server.server) with the environment variables KUBEOPT_API_URL, KUBEOPT_USERNAME, and KUBEOPT_PASSWORD. After restarting the client, ask questions in plain English about Kubernetes costs.

Key features of Kubeopt

  • Six MCP tools for querying cost and cluster data
  • Connects to Azure AKS, AWS EKS, and Google GKE
  • Runs 16 optimization algorithms (rightsizing, HPA, storage, etc.)
  • Generates actionable implementation plans with kubectl commands
  • Provides per-pod cost breakdowns filterable by namespace
  • Supports on-demand fresh analyses with polling

Use cases of Kubeopt

  • Query top cost savings opportunities across all clusters
  • Identify the most expensive pods in a specific namespace
  • Get a summary of total Kubernetes spend for the current month
  • Check optimization scores for a staging cluster
  • Trigger a fresh analysis on a specific cluster and receive results

よくある質問

1

What tools does the MCP server expose?

It exposes six tools: `list_clusters`, `get_cost_summary`, `get_cluster_analysis`, `get_recommendations`, `analyze_cluster`, and `get_pod_costs`.

2

What are the prerequisites for using the MCP server?

A running KubeOpt instance (locally via `python main.py` or deployed on Railway) and a Python virtual environment with dependencies installed (`pip install -r requirements.txt`).

3

Which cloud providers are supported?

KubeOpt supports Azure AKS, AWS EKS, and Google GKE.

4

What transport does the MCP server use?

It uses stdio transport.

5

Is authentication required to connect to the MCP server?

Yes, the configuration requires the environment variables `KUBEOPT_USERNAME` and `KUBEOPT_PASSWORD` for authentication against the KubeOpt API.

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