OCM MCP Server

Overview

The Ocm Mcp server bridges Red Hat OpenShift Cluster Manager (OCM) with AI assistants that speak the Model Context Protocol. It translates OCM API calls into MCP resources, allowing an AI assistant to discover cluster information, invoke management operations, and retrieve configuration data without writing custom adapters. This eliminates the need for developers to build bespoke integrations for each OCM endpoint, streamlining AI‑driven automation across large Kubernetes deployments.

At its core, the server exposes OCM as a set of MCP resources and tools. When an AI client requests cluster details, the server fetches data from OCM’s REST API and returns it as structured JSON. It also provides tools for actions such as creating, updating, or deleting clusters, and for querying subscription or billing information. The server’s transport layer is configurable: it can communicate over standard stdio for local development or expose an HTTP endpoint that supports Server‑Sent Events (SSE) for remote, long‑lived connections. This flexibility lets teams embed the MCP server in CI/CD pipelines, chatbots, or custom dashboards with minimal friction.

Key capabilities include:

• Unified OCM access: All OCM operations are available through a single MCP interface, removing the cognitive load of remembering multiple REST endpoints.
• Transport agnostic: The server supports both stdio (ideal for local testing) and SSE, making it suitable for cloud‑hosted services or edge deployments.
• Secure token handling: It accepts OCM offline tokens and an access‑token URL, ensuring that credentials are never exposed in plain text while still allowing automated authentication.
• Extensible configuration: By exposing environment variables and JSON‑based server definitions, developers can tailor the server to different environments (dev, staging, prod) without code changes.

Typical use cases span from automated cluster provisioning in a GitOps workflow to real‑time monitoring dashboards that answer natural‑language queries about cluster health. For example, an AI assistant can ask, “Show me all clusters with high CPU usage,” and the MCP server will retrieve the relevant OCM metrics, format them for the assistant, and return a concise report. In another scenario, an AI‑powered chatbot could guide users through the steps to upgrade a cluster, leveraging the server’s toolset to perform API calls on behalf of the user.

What sets Ocm Mcp apart is its tight coupling to OpenShift Cluster Manager, a platform many enterprises already rely on for managing multi‑cluster environments. By providing a ready‑made MCP layer, it removes the barrier of learning OCM’s intricacies and accelerates the integration of AI assistants into existing DevOps toolchains. The result is a smoother, more secure workflow where developers can focus on business logic while the server handles authentication, transport, and data mapping.