Kubernetes MCP Server

The k8s‑mcp‑server is a lightweight, Kubernetes‑native implementation of the Model Context Protocol (MCP). It addresses the growing need for AI assistants to operate seamlessly within cloud‑native environments by exposing a standardized set of MCP endpoints that interact directly with Kubernetes resources. By running as a pod inside a cluster, the server can read and modify configuration objects, launch workloads, and expose custom prompts—all without requiring external networking or complex authentication plumbing.

At its core, the server implements three main MCP capabilities: resources, tools, and prompts. The resource endpoint allows an AI client to query, create, or update any Kubernetes object (Deployments, Services, ConfigMaps, etc.) using familiar MCP payloads. This means an assistant can, for example, spin up a new microservice or adjust replica counts simply by issuing a single MCP command. The tool interface exposes cluster‑level operations such as scaling, rolling updates, or namespace management, enabling AI workflows to orchestrate infrastructure changes in a declarative manner. Finally, the prompt endpoint lets developers inject context‑specific instructions or templates that the assistant can retrieve and apply when interacting with the cluster, ensuring consistent behavior across different projects.

The server’s design emphasizes security and scalability. It leverages Kubernetes’ native RBAC to control which AI agents can perform specific actions, and it supports TLS termination for secure communication. Because the MCP protocol is stateless, the server can be replicated or autoscaled behind a load balancer without losing session information—a critical feature for production‑grade AI services that must handle many concurrent requests.

Typical use cases include automated deployment pipelines, on‑the‑fly debugging of production issues, and conversational configuration management. For instance, a developer could ask an AI assistant to “scale the payment service to 10 replicas” and receive instant feedback once the change propagates through the cluster. In research environments, the server can expose experimental models or data sets as Kubernetes resources, allowing AI agents to discover and utilize them without manual configuration.

Overall, the k8s‑mcp‑server provides a unified, protocol‑driven interface that brings AI assistants into the heart of Kubernetes operations. By abstracting away low‑level API calls and exposing high‑level, intent‑driven actions, it empowers developers to build smarter, more autonomous tooling that reacts directly to the state of their cloud infrastructure.