mcp-k8s-go MCP Server

What does “mcp-k8s-go” MCP Server do?

The mcp-k8s-go MCP Server is a Golang-based implementation of the Model Context Protocol (MCP) designed to connect AI assistants with Kubernetes clusters. It acts as a middleware between AI agents and Kubernetes APIs, allowing AI-driven workflows to interact programmatically with Kubernetes resources. By exposing cluster operations and contextual data, mcp-k8s-go enables tasks such as querying cluster status, managing deployments, inspecting pods, and more—all through a standardized MCP interface. This greatly enhances developer productivity by allowing AI assistants to automate, monitor, and manage Kubernetes environments seamlessly, making cloud-native operations more accessible and efficient.

List of Prompts

No prompt templates are mentioned in the available documentation or codebase.

List of Resources

No explicit resources are described in the available documentation or codebase.

List of Tools

No detailed tool definitions are directly exposed in the accessible code or documentation.

Use Cases of this MCP Server

• Kubernetes Cluster Management
  Developers can automate routine tasks such as scaling deployments, rolling updates, and monitoring pod status using AI assistants connected via the MCP server.
• AI-Powered Cluster Insights
  AI assistants can provide real-time insights into cluster health, resource utilization, and error diagnostics by querying the Kubernetes API through mcp-k8s-go.
• Automated Troubleshooting
  Enable AI-driven workflows to detect and potentially remediate common cluster issues by integrating with the server.
• Enhanced DevOps Automation
  Use the MCP server as a bridge for AI-driven CI/CD pipelines and operational workflows within Kubernetes environments.
• Secure Access Mediation
  The server can act as a controlled gateway, allowing only authorized AI agents to perform actions on the Kubernetes cluster.

How to set it up

Windsurf

1. Ensure prerequisites, such as Node.js and Docker, are installed.
2. Open Windsurf’s configuration file (typically windsurf.config.json).
3. Add the mcp-k8s-go MCP server to the mcpServers section:

   {
     "mcpServers": {
       "mcp-k8s-go": {
         "command": "npx",
         "args": ["@strowk/mcp-k8s@latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the setup by checking server logs or running a test connection.

Securing API Keys
Use environment variables to store sensitive credentials:

{
  "mcpServers": {
    "mcp-k8s-go": {
      "command": "npx",
      "args": ["@strowk/mcp-k8s@latest"],
      "env": {
        "KUBECONFIG": "/path/to/kubeconfig"
      }
    }
  }
}

Claude

1. Ensure prerequisites are installed.
2. Open Claude’s configuration file.
3. Add the mcp-k8s-go server:

   {
     "mcpServers": {
       "mcp-k8s-go": {
         "command": "npx",
         "args": ["@strowk/mcp-k8s@latest"]
       }
     }
   }
   

4. Save and restart Claude.
5. Confirm server availability through Claude’s interface.

Securing API Keys

{
  "mcpServers": {
    "mcp-k8s-go": {
      "command": "npx",
      "args": ["@strowk/mcp-k8s@latest"],
      "env": {
        "KUBECONFIG": "/path/to/kubeconfig"
      }
    }
  }
}

Cursor

1. Install prerequisites.
2. Edit Cursor’s settings file.
3. Add the MCP server entry:

   {
     "mcpServers": {
       "mcp-k8s-go": {
         "command": "npx",
         "args": ["@strowk/mcp-k8s@latest"]
       }
     }
   }
   

4. Save changes and restart Cursor.
5. Validate by checking MCP server connectivity.

Securing API Keys

{
  "mcpServers": {
    "mcp-k8s-go": {
      "command": "npx",
      "args": ["@strowk/mcp-k8s@latest"],
      "env": {
        "KUBECONFIG": "/path/to/kubeconfig"
      }
    }
  }
}

Cline

1. Make sure all dependencies are installed.
2. Open Cline’s config file.
3. Add the following MCP server configuration:

   {
     "mcpServers": {
       "mcp-k8s-go": {
         "command": "npx",
         "args": ["@strowk/mcp-k8s@latest"]
       }
     }
   }
   

4. Save the file and restart Cline.
5. Test the server through Cline’s interface.

Securing API Keys

{
  "mcpServers": {
    "mcp-k8s-go": {
      "command": "npx",
      "args": ["@strowk/mcp-k8s@latest"],
      "env": {
        "KUBECONFIG": "/path/to/kubeconfig"
      }
    }
  }
}

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:

{
  "mcp-k8s-go": {
    "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 “mcp-k8s-go” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the available information, the mcp-k8s-go MCP server provides a Kubernetes interface via MCP but lacks explicit documentation for prompts, resources, and sampling. The setup and security guidance are clear. I would rate this MCP server a 5/10 for general usability and documentation completeness, primarily due to missing details on MCP-specific resources and tools.

MCP Score