Multi-Cluster MCP Server

Multi-Cluster MCP server

The Multi-Cluster MCP Server provides a robust gateway for Generative AI (GenAI) systems to interact with multiple Kubernetes clusters through the Model Context Protocol (MCP). It facilitates comprehensive operations on Kubernetes resources, streamlined multi-cluster management, and delivered interactive cluster observability.

🚀 Features

🛠️ MCP Tools - Kubernetes Cluster Awareness

✅ Retrieve resources from the hub cluster (current context)
✅ Retrieve resources from the managed clusters
✅ Connect to a managed cluster using a specified ClusterRole
✅ Access resources across multiple Kubernetes clusters(via Open Cluster Management)
❌ Retrieve and analyze metrics, logs, and alerts from integrated clusters
❌ Interact with multi-cluster APIs, including Managed Clusters, Policies, Add-ons, and more

📦 Prompt Templates for Open Cluster Management (Planning)

Provide reusable prompt templates tailored for OCM tasks, streamlining agent interaction and automation

📚 MCP Resources for Open Cluster Management (Planning)

Reference official OCM documentation and related resources to support development and integration

📌 How to Use

Use with MCP Inspector

mcp dev ./src/multicluster_mcp_server/__main__.py 

Configure the server using the following snippet:

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

Note: Ensure kubectl is installed. By default, the tool uses the KUBECONFIG environment variable to access the cluster. In a multi-cluster setup, it treats the configured cluster as the hub cluster, accessing others through it.

License

This project is licensed under the MIT License.

This server cannot be installed

security - not tested

license - not found

quality - not tested

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A gateway for Generative AI systems to interact with multiple Kubernetes clusters through Model Context Protocol, enabling comprehensive Kubernetes resource operations and multi-cluster management.

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