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Python Testing Tools MCP Server

@jazzberry-ai

Repository

Basic information

Category

Other

Transports

stdio

Author

jazzberry-ai

Submitted by

Marco Dewey

About Python Testing Tools MCP Server

An advanced Model Context Protocol (MCP) server that provides AI-powered Python testing tools. This project leverages both Google's Gemini AI and BAML (Boundary ML) to intelligently generate comprehensive unit tests and perform sophisticated fuzz testing on Python code.

#jazzberry#software-testing#python#fuzzing#coverage#unit-test#mutation-test#testing#test#baml

Python Testing Tools MCP Server config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

Python Testing Tools MCP Server tools

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Overview

What is Python Testing Tools MCP Server?

It is an MCP server that generates unit tests, fuzz inputs, and coverage-focused tests for Python files using Google Gemini AI. It is designed for developers who want automated, AI-powered test generation integrated with Claude Code.

How to use Python Testing Tools MCP Server?

Install dependencies from requirements.txt, set the GEMINI_API_KEY environment variable, and run python mcp_server.py. For integration with Claude Code, use FastMCP to install the server, then configure it in .claude.json. After starting Claude Code, use commands like "create unit tests for @file.py", "generate comprehensive coverage tests for @file.py", or "fuzz test the function_name in @file.py".

Key features of Python Testing Tools MCP Server

  • AI-powered unit test generation with 4–6 test cases per function
  • Intelligent fuzz testing with 20 diverse inputs including edge cases
  • Advanced AST analysis for maximum code coverage
  • Built-in coverage measurement using coverage.py
  • Proper unittest framework with exception testing and safe input parsing
  • Automatic import resolution and formatted test file creation

Use cases of Python Testing Tools MCP Server

  • Automatically generate unit tests for all functions in a Python file
  • Perform intelligent fuzz testing to find crashes and edge cases
  • Create comprehensive coverage tests to achieve high branch and path coverage
  • Integrate AI-driven test generation into a Claude Code workflow

Frequently asked questions

1

What AI model does the server use?

It uses Google Gemini, defaulting to the `gemini-2.5-flash` model. You can set the `GEMINI_MODEL` environment variable to choose a different Gemini model.

2

How do I set up authentication?

Set the `GEMINI_API_KEY` environment variable with your Google Gemini API key. This is required for AI-powered test generation.

3

What are the runtime dependencies?

Python 3 with a virtual environment, dependencies from `requirements.txt`, and the BAML configuration in `baml_src/main.baml`. The server also requires coverage.py for coverage reports.

4

What transport protocol does the server use?

It uses the Model Context Protocol (MCP) over stdio. It can be run standalone with `python mcp_server.py` or integrated as an MCP server in Claude Code using FastMCP.

5

Where does the server output test files?

Test files are created in the same folder as the input Python file (the demo folder in the examples). The server reads files from the local filesystem based on the provided file path.

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