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Kubeopt

@kubeopt

Repository

About Kubeopt

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

Basic information

Category

Other

Transports

stdio

Publisher

kubeopt

Submitted by

SrinivasK

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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

FAQ from Kubeopt

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.

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).

Which cloud providers are supported?

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

What transport does the MCP server use?

It uses stdio transport.

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.

Comments

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