MKP - Model Kontext Protocol Server

Overview

MKP (Model Kontext Protocol) is a lightweight, production‑ready MCP server designed to bridge LLM‑powered applications and Kubernetes clusters. By exposing a set of declarative tools over the MCP protocol, it lets AI assistants query and manipulate Kubernetes resources without needing to embed kubectl or Helm logic. The server acts as a trusted proxy, translating high‑level AI commands into native Kubernetes API calls while handling authentication, rate limiting, and error reporting in a consistent manner.

The core value of MKP lies in its unstructured, pluggable implementation. Leveraging Kubernetes’ API Machinery, it can interact with any resource type—built‑in or custom—without hardcoding schemas. This means that as new CRDs appear in a cluster, the server automatically supports them, ensuring long‑term compatibility. Developers can therefore give an AI assistant commands like “list all deployments in the production namespace” or “apply a new Ingress resource,” and the assistant will receive accurate, up‑to‑date information directly from the cluster.

Key capabilities include:

• Resource discovery: List clustered, namespaced, or subresources such as status, logs, and scale.
• CRUD operations: Create or update resources via the tool for both cluster‑wide and namespaced objects.
• Command execution: Run arbitrary commands inside pods with configurable timeouts, enabling dynamic debugging or data extraction.
• Rate limiting: Built‑in protection against excessive API calls keeps the cluster stable and prevents accidental denial of service.
• Native Go integration: Written in Go, the server shares language and library compatibility with Kubernetes itself, delivering low latency and strong type safety.

In real‑world workflows, MKP empowers AI assistants to become first‑class operators in DevOps pipelines. For example, a chatbot can automatically patch a failing deployment, retrieve pod logs for troubleshooting, or deploy a new custom resource when prompted. Because the server exposes only the necessary subset of Kubernetes functionality, it can be safely deployed in secure environments where direct API access is restricted.

Overall, MKP offers a clean, extensible bridge between LLMs and Kubernetes, reducing boilerplate, improving reliability, and allowing developers to focus on higher‑level automation rather than plumbing details.