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Kubeopt MCP Server

@kubeopt

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

kubeopt

提交者

SrinivasK

关于 Kubeopt MCP Server

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 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "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 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 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

常见问题

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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