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MCP

Rafay MCP Server Phase 2: From Visibility to Lifecycle Management

In June we introduced the Rafay MCP Server, giving MCP-compatible AI clients secure, RBAC-aware, read-only access to your Rafay platform resources. That first release was about visibility and troubleshooting: listing clusters, inspecting blueprint versions, and answering operational questions in natural language.

Phase 2 takes the next step. Coming in the upcoming v4.3 release, the Rafay MCP Server can now create, update, delete, and publish a set of platform resources, letting authorized users move from asking questions about their environment to acting on it, without leaving their AI client and without stepping outside Rafay's governance controls. For the full list of changes, see the v4.3 release notes.

Bring Rafay Into Your AI Workflows with the Rafay MCP Server

AI assistants are now part of everyday work for platform, DevOps, and SRE teams. We use them to debug code, make sense of configuration, and understand how systems behave. But when it comes to managing Kubernetes clusters and platform infrastructure, these assistants hit a wall: they have no secure, real-time view of your environment.

Without a secure window into your actual operational state AI tools are forced to guess rely on stale data or require engineers to manually copy paste massive YAML files and CLI outputs into chat windows.

To bridge this gap without compromising on security, we are thrilled to introduce the Rafay MCP Server.

Interact with Your Rafay Managed Kubernetes Clusters Using MCP-compatible AI clients

The Model Context Protocol (MCP) is an open standard that enables AI assistants to securely interact with external tools and systems. When used with Kubernetes, MCP allows an AI assistant to execute operations (for example, kubectl commands), retrieve live cluster state, and reason about results without requiring users to manually copy and paste output into a chat interface.

This blog uses Claude Desktop as an example AI assistant. The same approach applies to any MCP-compatible AI client.

For platform administrators, this capability enables controlled, auditable, and policy-driven AI-assisted cluster operations.

For production environments, the recommended approach is to run the MCP server locally and connect to your Kubernetes cluster using a Rafay Zero Trust Kubectl Access (ZTKA) kubeconfig.

In this model:

  • The MCP server runs on the administrator’s workstation
  • Cluster access is established through Rafay’s ZTKA secure relay
  • No inbound access to the cluster is required
  • No VPN tunnels or exposed Kubernetes API endpoints are needed

This architecture aligns with zero-trust security principles and enterprise compliance requirements.