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Contributing to Kubeflow MCP Server

Thank you for your interest in contributing! Checkout the general Kubeflow contributing guidelines here.

We encourage the judicious use of AI/LLM tools; please refer to the Kubeflow AI Policy for more information.

Requirements

Getting Started

  1. Fork the repository

  2. Clone your fork:

    git clone https://github.com/<your-username>/kubeflow-mcp-server.git
cd kubeflow-mcp-server

  3. Set up development environment:

    make install-dev

  4. Create a branch:

    git checkout -b feat/your-feature

Development

The Kubeflow MCP Server project includes a Makefile with several helpful commands to streamline your development workflow.

Coding Style

Before creating git commits, ensure you have installed pre-commit hooks:

uv run pre-commit install

The pre-commit hooks ensure code quality and consistency (linting and formatting with ruff). They are also executed in CI.

To run verification checks locally:

make verify

Testing

The project includes unit tests to ensure code quality and functionality.

Unit Testing

To run unit tests locally, use the following make command:

make test-python

Commit Messages

We use Conventional Commits:

<type>(<scope>): <description>

[optional body]

Types: feat, fix, revert, chore

Examples:

  • feat(trainer): add create_training_job tool
  • fix(core): handle timeout in k8s client
  • docs: update README with usage examples

Pull Request Process

  1. Update tests for your changes
  2. Ensure all checks pass (make verify and make test-python)
  3. Update documentation if needed
  4. Request review from maintainers

Areas Open for Contribution

  • OptimizerClient tools - Hyperparameter optimization integration
  • ModelRegistryClient tools - Model registry integration
  • Documentation - Examples and tutorials
  • Testing - Increase test coverage

Code of Conduct

This project follows the Kubeflow Code of Conduct.

Questions?

Open an issue or reach out to maintainers on the CNCF Slack #kubeflow-ml-experience channel.