test-coach

A new project currently under development.

Overview

This project implements an AI-powered testing and code-fixing server using FastMCP and OpenAI. It provides tools to generate tests, run them under coverage, and suggest code fixes.

Setup

1. Create and activate a Python virtual environment:

   python -m virtualenv .venv
   source .venv/bin/activate    # On Windows: `.\\.venv\\Scripts\\activate`

2. Install dependencies:

   pip install -r requirements.txt

3. Copy the example environment file and configure your variables:

   cp .env.example .env      # On Windows: `copy .env.example .env`
   # Then edit .env and set your values

4. (Optional) Remove any embedded credentials from Git config:

   git remote set-url origin https://github.com/$GITHUB_USER/test-coach.git

Environment Variables

Store all sensitive keys and tokens in the .env file. See .env.example for required names.

How it works

• Server (fastmcp_server) exposes three tools via standard I/O:

  • generate_tests(module: str) -> str
    Generates pytest tests for a given Python module using the OpenAI ChatCompletion API.
  • run_tests() -> dict
    Runs tests under coverage, produces a JSON coverage report, and returns it.
  • fix_code(failing_test: str, error: str) -> str
    Prompts OpenAI to suggest code or test fixes so that pytest passes.

• Pipe (fastmcp_server/pipe.py) handles communication with the OpenAI API:

  • Loads OPENAI_API_KEY and optional OPENAI_MODEL from .env.
  • Defines codex_query(prompt, temperature) wrapping openai.ChatCompletion.create.

• Tools (fastmcp_server/tools.py) implement the logic of each tool, decorated with @mcp.tool().

• Server entrypoint (fastmcp_server/server.py):

  • Imports and registers all tools, then calls mcp.run() to start listening on stdio.

• Sample library (sample_lib/calculator.py) provides simple functions add(a, b) and divide(a, b) (with a deliberate ZeroDivisionError on b=0) to demonstrate the workflow.

Usage Example

1. Start the FastMCP server:

   python -m fastmcp_server.server

2. Generate tests for the sample module in another terminal:

   mcp-cli call generate_tests \
     --server stdio://$(pgrep -f fastmcp_server.server) \
     --json '{"module":"sample_lib.calculator"}'

3. Run tests and retrieve coverage:

   mcp-cli call run_tests --server stdio://$(pgrep -f fastmcp_server.server)

Tento projekt, nazvaný "test-coach", je nástroj pro automatizované testování a opravy kódu s využitím AI (konkrétně OpenAI API). Hlavní funkce zahrnují:

Generování automatických testů pro Python moduly

Spouštění testů s analýzou pokrytí kódu

Navrhování oprav kódu na základě neúspěšných testů a chybových hlášek