Used for demonstrating multi-cluster Kubernetes operations through recorded terminal sessions that can be viewed within the documentation.
Provides full support for kubectl operations to interact with Kubernetes clusters, allowing creation, updating, and listing of resources such as Deployments, Pods, and Services across multiple clusters.
Provides platform-specific configuration instructions for installing and configuring the MCP server on macOS systems.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Multi-Cluster MCP Server list all pods across all managed clusters"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
The OCM 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.
✅ 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
Provide reusable prompt templates tailored for OCM tasks, streamlining agent interaction and automation
Reference official OCM documentation and related resources to support development and integration
Configure the server using the following snippet:
{
"mcpServers": {
"multicluster-mcp-server": {
"command": "npx",
"args": [
"-y",
"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.
Related MCP server: kube-mcp
This project is licensed under the MIT License.
Retrieves a list of Kubernetes clusters (also known as managed clusters or spoke clusters).
Inputs: no named parameters.
Generates the 'KUBECONFIG' for the managed cluster and binds it to the specified ClusterRole (default: cluster-admin).
Inputs: cluster (required), cluster_role.
Securely run a kubectl command or apply YAML. Provide either 'command' or 'yaml'.
Inputs: cluster, command, yaml.
Query Prometheus metrics from a specific cluster and format the results for Recharts visualization.
Inputs: ql (required), cluster, data_type, end, +4 more.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap: clusters lists clusters, connect_cluster generates access credentials, kube_executor runs kubectl commands, and prometheus queries metrics. The descriptions clearly differentiate their functions, eliminating any ambiguity for an agent.
The naming is mixed with no consistent pattern: clusters is a noun, connect_cluster uses verb_noun, kube_executor uses a compound noun, and prometheus is a proper noun. While readable, the lack of a uniform convention (e.g., all verb_noun or all nouns) reduces predictability.
With 4 tools, the count is slightly low but reasonable for a multi-cluster Kubernetes management server. It covers core operations like listing clusters, accessing them, executing commands, and monitoring metrics, though it could benefit from additional tools for more comprehensive management.
The toolset covers key areas (discovery, access, execution, monitoring) but has notable gaps for a multi-cluster domain, such as creating/deleting clusters, managing resources across clusters, or handling configurations. Agents can work around this for basic tasks, but advanced operations may be limited.
The Google GKE MCP server is a managed Model Context Protocol server that provides AI applications with tools to manage Google Kubernetes Engine (GKE) clusters and Kubernetes resources. It exposes a structured, discoverable interface that allows AI agents to interact with GKE and Kubernetes APIs, enabling them to inspect cluster configurations, retrieve Kubernetes resource YAMLs, monitor operations like cluster upgrades, diagnose issues, and optimize costs—all without needing to parse text output or use complex kubectl commands.
MCP gateway: one connection for your AI apps, with tool policies, PII redaction and audit logs.
Zero-setup MCP gateway securely connecting AI to your tools with authentication and workflows
Connect MCP clients to 2,000+ AI models without managing provider API keys.
