Python Testing Tools MCP Server

@jazzberry-ai

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

jazzberry-ai

提交者

Marco Dewey

关于 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 配置

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该服务器的 README 中没有可解析的 MCP 配置块，请前往代码仓库查看安装说明。

代码仓库

Python Testing Tools MCP Server 工具

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概览

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

常见问题

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.

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.

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.

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.

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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