Kursk Ye Code2flow MCP Server

🚀 Code2Flow MCP Server

This project wraps the code2flow command-line tool into an MCP (Model Context Protocol) server. It allows AI applications to generate and access code call graphs through the standardized MCP protocol.

🚀 Quick Start

Prerequisites

• Python 3.7+
• Windows 11 or other supported operating systems
• PowerShell or other command-line terminals
• The code2flow command-line tool installed

Installation

1. Clone this repository

git clone https://github.com/kursk-ye/code2flow-mcp-server.git
cd code2flow-mcp-server

2. Create and activate a virtual environment (recommended)

python -m venv venv
.\venv\Scripts\Activate.ps1  # In PowerShell

3. Install dependencies

pip install -r requirements.txt

4. Install the code2flow command-line tool

pip install code2flow

Running the Server

Run directly with Python

1. Start the MCP server

python server.py

Run with MCP tools

1. Use the MCP Inspector tool

mcp dev server.py

2. Install it to Claude Desktop

mcp install server.py

3. Add it to the Cursor MCP configuration

"code2flow": {
  "command": "cmd",
  "args": [
    "/c",
    "python",
    "path/to/server.py"
  ]
}

✨ Features

• Analyze source code and generate call graphs.
• Support multiple programming languages (Python, JavaScript, Ruby, PHP).
• Provide services through the MCP protocol, making it easy to integrate with AI applications.
• Output images in PNG format.
• Offer version checking and code complexity analysis functions.

💻 Usage Examples

Basic Usage

import asyncio
from mcp.client import MCPClient

async def main():
    # Connect to the server
    client = MCPClient("http://localhost:8000")
    
    # Create a new session
    session = await client.create_session()
    
    # Call the tool to generate a call graph
    result = await session.call_tool("generate_call_graph", {
        "source_paths": ["path/to/your/code"],
        "language": "Python"
    })
    
    # Check the code2flow version
    version = await session.call_tool("check_code2flow_version")
    
    # Analyze code complexity
    complexity = await session.call_tool("analyze_code_complexity", {
        "source_paths": ["path/to/your/code"]
    })
    
    print(f"Call graph generation result: {result}")
    print(f"code2flow version: {version}")
    print(f"Code complexity analysis result: {complexity}")

asyncio.run(main())

📚 Documentation

Available Tools

The server provides the following MCP tools:

1. generate_call_graph - Generate a code call graph.
2. check_code2flow_version - Check the code2flow version.
3. analyze_code_complexity - Analyze code complexity.

Available Resources

The server provides the following MCP resources:

1. help://code2flow - Help documentation.
2. languages://supported - List of supported languages.
3. call-graph://Image ID - Generated call graph images.

Configuration Options

When generating a call graph, you can use the following configuration options:

• source_paths - Specify the paths of source files to be analyzed.
• language - Specify the code language (supports Python, JavaScript, Ruby, PHP).

File Structure

The project contains the following files:

• server.py - Main server code.
• requirements.txt - List of dependencies.
• README.md - Project description document.

📄 License

This project uses the MIT license.