Project 6: MCP Server for Kubeflow SDK

The Kubeflow SDK gives AI practitioners a clean Python interface to submit, monitor, and manage distributed training jobs on Kubernetes via TrainerClient. However, LLM-based developer tools currently have no standardized way to access this runtime context — they cannot see a TrainJob's status, stream its logs, or reason about why a job failed. This project extends the existing MCP server MVP (tracked in kubeflow/community#936) for the Kubeflow Trainer SDK. The goal is to deliver a production-ready MCP server that exposes the full TrainJob lifecycle through well-typed, LLM-accessible tools — enabling AI assistants to actively help developers debug, monitor, and operate their Kubeflow training workloads in real time.