✨ Features

📦 Installation

Quick Install

```bash

# Clone the repository
git clone https://github.com/ravikant1918/neurodev-mcp.git
cd neurodev-mcp

# Create virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\\Scripts\\activate

# Install the package
pip install -e .
\`\`\`

### **Verify Installation**

\`\`\`bash
# Run tests (should show 15/15 passing)
python test_installation.py

# Test the server
python -m neurodev_mcp.server
\`\`\`

<details>
<summary><b>📁 Project Structure</b> (click to expand)</summary>

\`\`\`
neurodev-mcp/
├─ neurodev_mcp/              # 📦 Main package
│   ├─ __init__.py            # Package exports
│   ├─ server.py              # MCP server entry point
│   ├─ analyzers/             # 🔍 Code analysis
│   │   ├─ __init__.py
│   │   └─ code_analyzer.py   # Multi-tool static analysis
│   ├─ generators/            # 🧪 Test generation
│   │   ├─ __init__.py
│   │   └─ test_generator.py  # AST-based test creation
│   └─ executors/             # ▶️ Test execution
│       ├─ __init__.py
│       └─ test_executor.py   # Test running & formatting
├─ pyproject.toml             # Project configuration
├─ README.md                  # This file
├─ test_installation.py       # Installation validator
├─ examples.py                # Usage examples
└─ requirements.txt           # Dependencies

🚀 Quick Start

Step 1: Configure Your MCP Client

{
  "mcpServers": {
    "neurodev-mcp": {
      "command": "/absolute/path/to/neurodev-mcp/.venv/bin/python",
      "args": ["-m", "neurodev_mcp.server"]
    }
  }
}

{
  "neurodev-mcp": {
    "command": "python",
    "args": ["-m", "neurodev_mcp.server"]
  }
}

# Using the module
python -m neurodev_mcp.server

# Or as a command (if installed)
neurodev-mcp

Step 2: Restart Your Client

Restart Claude Desktop or reload VSCode to load the server.

Step 3: Start Using! 🎉

Try these commands with your AI assistant:

• "Review this Python code for issues"
• "Generate unit tests for this function"
• "Run these tests with coverage"
• "Format this code to PEP8 standards"

🌐 Transport Options

NeuroDev MCP supports multiple transport protocols for different use cases:

STDIO (Default) - Local CLI

Perfect for local development with MCP clients like Claude Desktop or Cline:

# Default STDIO transport
neurodev-mcp

# Or explicitly specify STDIO
neurodev-mcp --transport stdio

Configuration (Claude Desktop):

{
  "mcpServers": {
    "neurodev-mcp": {
      "command": "neurodev-mcp",
      "args": ["--transport", "stdio"]
    }
  }
}

SSE (Server-Sent Events) - Web Integration

For web-based integrations and HTTP streaming:

# Run with SSE on default port (8000)
neurodev-mcp --transport sse

# Custom host and port
neurodev-mcp --transport sse --host 0.0.0.0 --port 3000

Endpoints:

• SSE Stream: http://localhost:8000/sse
• Messages: http://localhost:8000/messages (POST)

Web Client Example:

const sse = new EventSource('http://localhost:8000/sse');

sse.onmessage = (event) => {
  const data = JSON.parse(event.data);
  console.log('Received:', data);
};

// Send message
fetch('http://localhost:8000/messages', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    method: 'tools/call',
    params: {
      name: 'code_review',
      arguments: { code: 'def test(): pass', analyzers: ['pylint'] }
    }
  })
});

Transport Comparison

🛠️ Available Tools

1. code_review

🔍 Comprehensive code analysis with multiple static analysis tools

Input:

{
  "code": "def calculate(x):\n    return x * 2",
  "analyzers": ["pylint", "flake8", "mypy", "bandit", "radon", "ast"]
}

Output:

• Detailed issue reports from each analyzer
• Security vulnerabilities
• Complexity metrics
• Code quality scores
• Line-by-line suggestions

2. generate_tests

🧪 Intelligent pytest test generation using AST analysis

Input:

{
  "code": "def add(a: int, b: int) -> int:\n    return a + b",
  "module_name": "calculator",
  "save": false
}

Output:

• Complete pytest test suite
• Multiple test cases (happy path, edge cases, exceptions)
• Type validation tests
• Ready-to-run test code

3. run_tests

▶️ Execute pytest tests with coverage reporting

Input:

{
  "test_code": "def test_add():\n    assert add(1, 2) == 3",
  "source_code": "def add(a, b):\n    return a + b",
  "timeout": 30
}

Output:

• Pass/fail status
• Coverage percentage
• Line coverage details
• Execution time
• Detailed stdout/stderr

4. format_code

🎨 Auto-format Python code to PEP8 standards

Input:

{
  "code": "def   messy(  x,y  ):\n        return x+y",
  "line_length": 88
}

Output:

• Beautifully formatted code
• PEP8 compliant
• Consistent style
• Change detection

💡 Usage Examples

Example 1: Complete Code Review Workflow

You: "Review this code for issues and security problems"

[paste code]

AI: [Uses code_review tool]
    → Finds 3 style issues
    → Detects 1 security vulnerability
    → Suggests complexity improvements
    
You: "Fix those issues and show me the updated code"

AI: [Provides fixed code with explanations]

Example 2: Test Generation & Execution

You: "Generate tests for this function and run them"

def divide(a: float, b: float) -> float:
    if b == 0:
        raise ValueError("Cannot divide by zero")
    return a / b

AI: [Uses generate_tests tool]
    → Creates 5 test cases
    → Includes edge cases (zero, negative numbers)
    → Tests exception handling
    
    [Uses run_tests tool]
    → 5/5 tests passing ✓
    → 100% code coverage
    → All edge cases handled

Example 3: Code Formatting

You: "Format this messy code"

def   calculate(  x,y,z  ):
        result=x+y+z
        if result>10:
                    return   True
        return False

AI: [Uses format_code tool]
    → Applies black formatting
    → Returns clean, PEP8-compliant code

def calculate(x, y, z):
    result = x + y + z
    if result > 10:
        return True
    return False

📋 Requirements

Python: 3.8 or higher

🧪 Development

Running Tests

# Run installation tests
python test_installation.py

# Run examples
python examples.py

# Run pytest (if you add tests)
pytest

Using as a Library

from neurodev_mcp import CodeAnalyzer, TestGenerator, TestExecutor
import asyncio

# Analyze code
code = "def hello(): print('world')"
result = asyncio.run(CodeAnalyzer.analyze_ast(code))

# Generate tests
tests = TestGenerator.generate_tests(code, "mymodule")

# Run tests
output = TestExecutor.run_tests(test_code, source_code)

❓ Troubleshooting

# Reinstall the package
pip install -e .

# Verify installation
python -c "from neurodev_mcp import CodeAnalyzer; print('✓ OK')"

# Run installation tests
python test_installation.py

🤝 Contributing

Contributions are welcome! Here's how:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes
4. Run tests: python test_installation.py
5. Commit: git commit -m 'Add amazing feature'
6. Push: git push origin feature/amazing-feature
7. Open a Pull Request

Future Enhancements

• Additional analyzers (pydocstyle, vulture)
• Result caching for performance
• Configuration file support
• Web dashboard
• Multi-language support
• CI/CD pipeline

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with the Model Context Protocol
• Powered by pylint, flake8, mypy, bandit, radon
• Testing with pytest
• Formatting with black

📞 Support

• 📖 Documentation: You're reading it!
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 📧 Email: [email protected]