Contributing Guide

Thank you for your interest in contributing to sktime-mcp. This project aims to provide a robust MCP layer for the sktime ecosystem.

Development Setup

  1. Fork and Clone:

    git clone https://github.com/sktime/sktime-mcp.git
cd sktime-mcp

  2. Environment: We recommend using a virtual environment.

    python3 -m venv .venv
source .venv/bin/activate

  3. Install Dependencies: Install the package in editable mode with all development and optional dependencies.

    pip install -e ".[dev,all]"

  4. Pre-commit Hooks: Enable pre-commit hooks to ensure code quality and formatting.

    pre-commit install

Code Quality Standards

We use ruff for linting and formatting.

  • Check Linting: ruff check .

  • Format Code: ruff format .

Ensure all new code is properly type-hinted and includes docstrings following the NumPy format.

Testing

The project uses pytest. All new features should be accompanied by unit or integration tests.

Running Tests

pytest tests/

Async Tests

Since the MCP server is heavily asynchronous, many tests use pytest-asyncio. Ensure your test environment is correctly configured.

Project Structure

  • src/sktime_mcp/server.py: The main entry point and tool dispatcher.

  • src/sktime_mcp/tools/: Implementation of individual MCP tools, grouped by category.

  • src/sktime_mcp/runtime/: State management, handle registry, and job execution.

  • src/sktime_mcp/data/: Data loading and formatting logic.

  • src/sktime_mcp/composition/: Pipeline validation and building.

Pull Request Process

  1. Create a new branch for your feature or bug fix.

  2. Ensure all tests pass and linting is clean.

  3. Update the documentation if you are adding or changing tools.

  4. Submit a PR with a clear description of the changes and references to any related issues.