Mcpunk

🚀 MCPunk Project Guide

MCPunk is a tool designed for interacting with large language models (LLMs) and specifically tailored for code analysis. It processes files in a project in chunks and parses and executes them through a toolchain.

✨ Features

• Code Awareness: Supports syntax highlighting, autocompletion, and error detection for Python and other languages.
• Executable Context: Allows direct execution of code snippets in an interactive environment.
• Result Presentation: Supports presenting analysis results in text, chart, or HTML formats.

🚀 Quick Start

1. Project Configuration: Create and edit the mcp.json file to specify the project structure and toolchain.
2. Start the Service: Launch the MCPunk service using the command line or the desktop application.
3. Code Interaction: Write, debug, and analyze code in the integrated development environment.

📚 Documentation

Toolchain

MCPunk provides the following core tools:

mcp

• File Management: Loads and manages source files in the project.
• Code Parsing: Generates code structure information using a lexical analyzer and syntax tree.
• Result Caching: Optimizes repeated calculations to improve performance.

mcpy

• Code Execution: Safely runs code snippets in a sandbox environment.
• Debugging Support: Provides breakpoints, variable monitoring, and call stack functionality.
• Result Handling: Processes and presents execution results, supporting multiple output formats.

💻 Usage Examples

mcp.json Configuration Example

# mcp.json 配置示例
{
  "include": ["src/**/*"],
  "exclude": ["tests", "venv"],
  "parsers": {
    "python": {
      "syntax": true,
      "autoComplete": true,
      "diagnostics": true
    }
  },
  "analyzers": {
    "complexity": {
      "threshold": 20
    },
    "dependency": {
      "track": true
    }
  }
}

Command-Line Operations

# 初始化项目
mcp init

# 启动服务
mcp start --port 8000

# 执行代码
mcpy examples/hello.py

Desktop Application

The desktop application allows users to intuitively view and manage project files and conduct interactive analysis.

System Requirements

Frequently Asked Questions

Installation Issues

• Ensure that Python and dependencies are correctly installed.
• Check the environment variable configuration to ensure that commands are executable.

Performance Issues

• Use the --no-cache option to disable caching for performance troubleshooting.
• Optimize the project configuration to exclude unnecessary files and analyzers.

Configuration Options

MCPunk supports configuration in the following ways:

• Environment Variables: Set variables such as MCPUNK_CACHE_SIZE.
• Command-Line Arguments: Specify configuration parameters at runtime.
• Configuration File: Use the mcp.json file to define project-specific settings.

Development Guide

Code Structure

mcpunk/
├── core/          # 核心算法和工具链实现
│   ├── parser.py      # 文件解析器
│   └── analyzer.py    # 代码分析器
├── interface/     # 用户界面组件
│   ├── cli.py        # 命令行接口
│   └── desktop.py    # 桌面应用框架
└── config/        # 配置管理模块
    └── mcp.json.example  # 示例配置文件

Contribution Guide

• Submit an issue to report problems.
• Submit a PR to fix bugs or add features.
• Participate in community discussions and propose improvement suggestions.

Future Development

MCPunk plans to add the following features in future versions:

• AI-Driven Development: Utilize LLMs for code generation and optimization.
• Real-Time Collaboration: Support multi - user online collaborative development.
• Cross - Platform Support: Optimize compatibility with different operating systems.

Reference Documents

• Official Documentation
• API Documentation
• Community Forum

Through the above guide, users can comprehensively understand the features and usage of MCPunk and fully unleash its potential in code analysis and interactive development.