Kubernetes MCP Server Integration

What does “Kubernetes” MCP Server do?

The Kubernetes MCP Server acts as a bridge between AI assistants and Kubernetes clusters, enabling AI-driven automation and management of Kubernetes resources. By exposing Kubernetes management commands through the Model Context Protocol (MCP), this server empowers developers and AI agents to perform tasks such as deploying applications, scaling services, and monitoring cluster health. With its integration, users can interact with Kubernetes clusters programmatically, execute common administrative tasks, and streamline DevOps workflows via natural language or AI-driven prompts. This powerful interface enhances development productivity, supports complex automation scenarios, and provides a standardized way for AI systems to interact with Kubernetes infrastructure.

List of Prompts

No prompt templates are mentioned in the available documentation.

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List of Resources

No explicit resources are described in the available documentation or repository files.

List of Tools

No specific tools are enumerated in the available documentation or server code listing.

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Use Cases of this MCP Server

• Kubernetes Cluster Management: Automate scaling, deployment, and configuration of applications within Kubernetes clusters, reducing manual DevOps workload.
• Resource Monitoring: Enable AI assistants to query the status of pods, services, and nodes, allowing for real-time health checks and reporting.
• Automated Rollouts: Use AI-driven commands to trigger rolling updates or rollbacks of deployments, ensuring seamless and controlled application releases.
• Configuration Management: Manage and update Kubernetes resource definitions (YAML manifests) directly through AI interfaces, improving configuration consistency and control.
• Incident Response: Allow for quick diagnosis and remediation of cluster issues via automated scripts or AI-generated commands, minimizing downtime.

How to set it up

Windsurf

1. Ensure Node.js and Bun are installed on your system.
2. Open Windsurf’s configuration file (typically windsurf.config.json).
3. Add the Kubernetes MCP Server to the mcpServers object:

   {
     "mcpServers": {
       "kubernetes-mcp": {
         "command": "npx",
         "args": ["@Flux159/mcp-server-kubernetes@latest"]
       }
     }
   }
   

4. Save the configuration file and restart Windsurf.
5. Verify the Kubernetes MCP Server is running from the Windsurf interface.

Securing API Keys Example:

{
  "mcpServers": {
    "kubernetes-mcp": {
      "command": "npx",
      "args": ["@Flux159/mcp-server-kubernetes@latest"],
      "env": {
        "KUBECONFIG": "/path/to/kubeconfig"
      },
      "inputs": {
        "cluster": "your-cluster-name"
      }
    }
  }
}

Claude

1. Install Node.js and Bun as prerequisites.
2. Open Claude’s configuration file.
3. Add the MCP Server:

   {
     "mcpServers": {
       "kubernetes-mcp": {
         "command": "npx",
         "args": ["@Flux159/mcp-server-kubernetes@latest"]
       }
     }
   }
   

4. Save and restart Claude.
5. Confirm the MCP Server is accessible in Claude.

Cursor

1. Make sure Node.js and Bun are installed.
2. Edit Cursor’s config (e.g., cursor.config.json).
3. Integrate the MCP Server as follows:

   {
     "mcpServers": {
       "kubernetes-mcp": {
         "command": "npx",
         "args": ["@Flux159/mcp-server-kubernetes@latest"]
       }
     }
   }
   

4. Save and restart Cursor.
5. Check the MCP Server status in Cursor.

Cline

1. Install Node.js and Bun.
2. Locate Cline’s configuration file.
3. Add the Kubernetes MCP Server:

   {
     "mcpServers": {
       "kubernetes-mcp": {
         "command": "npx",
         "args": ["@Flux159/mcp-server-kubernetes@latest"]
       }
     }
   }
   

4. Save changes and restart Cline.
5. Validate connectivity to the MCP Server.

Note: For all platforms, secure access to your Kubernetes cluster by specifying the KUBECONFIG path via the env object in your configuration. Place secrets (API tokens, kubeconfig paths) in environment variables rather than in plain JSON.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "kubernetes-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “kubernetes-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Between these two tables, I would rate this MCP server as a 5/10: It provides a well-known and valuable integration (Kubernetes management), is open-source and popular, but lacks detailed documentation on prompt templates, explicit resources, and tool listing.

MCP Score