Multicluster MCP Server

What does “Multicluster” MCP Server do?

The Multicluster MCP Server acts as a gateway for GenAI systems to interact with multiple Kubernetes clusters via the Model Context Protocol (MCP). By exposing Kubernetes cluster data and operations through MCP, the server enables AI assistants and developer tools to programmatically access, manage, and orchestrate resources across several clusters. This integration enhances development workflows by allowing tasks such as querying cluster states, deploying workloads, monitoring resources, and automating DevOps processes, all from within AI-powered environments. The Multicluster MCP Server is designed to streamline cluster management, improve operational efficiency, and enable more intelligent automation in cloud-native application development.

List of Prompts

No prompt templates are mentioned or found in the provided repository.

List of Resources

No explicit resources are listed or described in the provided repository.

List of Tools

No tools or tool definitions were found in the available files of the repository.

Use Cases of this MCP Server

• Multi-cluster Kubernetes Management:
  Enables GenAI assistants to orchestrate operations across multiple Kubernetes clusters, such as deployments, scaling, and configuration changes.

• DevOps Automation:
  Facilitates the automation of CI/CD pipelines and infrastructure tasks by allowing AI systems to interact with and control multiple clusters in real time.

• Cloud Resource Monitoring:
  Assists in monitoring the health and status of resources distributed across several clusters, centralizing observability for platform engineers.

• Self-healing Infrastructure:
  AI agents can detect failures or anomalies across clusters and trigger remediation actions programmatically, improving resilience.

• Workflow Integration:
  Integrates cluster operations with development tools, making it possible to trigger complex workflows or gather context for LLM-based code suggestions.

How to set it up

Windsurf

1. Ensure Node.js is installed on your system.
2. Locate your Windsurf configuration file.
3. Add the Multicluster MCP Server to your mcpServers section using the JSON snippet below.
4. Save your configuration and restart Windsurf.
5. Verify setup by checking for successful connection to the MCP server.

{
  "mcpServers": {
    "multicluster-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "multicluster-mcp-server@latest"
      ]
    }
  }
}

Claude

1. Make sure Node.js is installed.
2. Open your Claude configuration file.
3. Insert the Multicluster MCP Server configuration under mcpServers.
4. Save changes and restart Claude.
5. Confirm the MCP server is reachable.

{
  "mcpServers": {
    "multicluster-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "multicluster-mcp-server@latest"
      ]
    }
  }
}

Cursor

1. Install Node.js if not already present.
2. Access the Cursor settings or configuration file.
3. Add the following JSON under mcpServers.
4. Save the file and restart Cursor.
5. Check the integration by invoking a sample MCP command.

{
  "mcpServers": {
    "multicluster-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "multicluster-mcp-server@latest"
      ]
    }
  }
}

Cline

1. Confirm Node.js installation.
2. Edit the Cline configuration file.
3. Integrate the Multicluster MCP Server with the JSON snippet below.
4. Save and restart Cline.
5. Validate the MCP server connection.

{
  "mcpServers": {
    "multicluster-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "multicluster-mcp-server@latest"
      ]
    }
  }
}

Securing API Keys

To secure API keys and sensitive information, use environment variables in your configuration:

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

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:

{
  "multicluster-mcp-server": {
    "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 “multicluster-mcp-server” to your actual MCP server name and replace the URL with your own MCP server URL.

Overview

Our opinion

The Multicluster MCP Server provides clear value for managing Kubernetes clusters with GenAI tools, but the repository currently lacks documentation on prompts, resources, and tools, and does not mention Roots or Sampling. Its setup instructions are present and clear, but the overall utility for AI workflows is not fully exposed in the repo.

Rating: 4/10

MCP Score