AI-powered interface for Kubeflow Training via Model Context Protocol.
Proposal: https://github.com/kubeflow/community/tree/master/proposals/936-kubeflow-mcp-server
⚠️ Note: This project is in early development. We currently accept PRs only after prior discussion on Slack — join#kubeflow-ml-experienceon the CNCF Slack. For more discussion, join on bi-weekly ML Experience WG call on Wednesdays.
This MCP server enables LLM agents (Claude, Cursor, etc.) to interact with Kubeflow Training through natural language. It wraps the Kubeflow SDK with MCP tools for fine-tuning, training job management, and monitoring.
| MCP Server | Kubeflow SDK | Python | Kubernetes |
|---|---|---|---|
| 0.1.x | ≥ 0.4.0 | 3.10 – 3.12 | ≥ 1.27 |
| Component | Status |
|---|---|
| Core Infrastructure | 🚧 In Progress |
| TrainerClient Tools | 🚧 In Progress |
| OptimizerClient Tools | ⬜ Planned (Contributors Welcome) |
| ModelRegistryClient Tools | ⬜ Planned (Contributors Welcome) |
| PipelinesClient Tools | ⬜ Planned (Contributors Welcome) |
| SparkClient Tools | ⬜ Planned (Contributors Welcome) |
| FeastClient Tools | ⬜ Planned (Contributors Welcome) |
# Install (trainer + optimizer included by default)
pip install kubeflow-mcp
# Install with hub or spark extras
pip install kubeflow-mcp[hub]
pip install kubeflow-mcp[spark]
# Run
kubeflow-mcp serve --clients trainerThe project uses uv and a Makefile to manage the development environment.
# Setup development environment
make install-dev
# Run verification (lint, format)
make verify
# Run unit tests
make test-pythonSee CONTRIBUTING.md for guidelines.
Apache License 2.0 - See LICENSE