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🧠 NeuroDev MCP Server

Intelligent Code Analysis, Test Generation & Execution

A powerful Model Context Protocol (MCP) server that supercharges your Python development workflow with AI-powered code review, intelligent test generation, and comprehensive test execution.

Features • Installation • Quick Start • Tools • Examples

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✨ Features

<table> <tr> <td width="50%">

🔍 Code Review

• 6 Powerful Analyzers
  • pylint - Code quality & PEP8
  • flake8 - Style enforcement
  • mypy - Type checking
  • bandit - Security scanning
  • radon - Complexity metrics
  • AST - Custom inspections
• Real-time issue detection
• Security vulnerability scanning
• Complexity & maintainability scores

</td> <td width="50%">

🧪 Test Generation

• Intelligent AST Analysis
  • Auto-generate pytest tests
  • Happy path coverage
  • Edge case handling
  • Exception testing
  • Type validation tests
• Supports functions & classes
• Type-hint aware

</td> </tr> <tr> <td width="50%">

▶️ Test Execution

• Comprehensive Testing
  • Isolated environment
  • Coverage reporting
  • Line-by-line analysis
  • Timeout protection
• Detailed pass/fail results
• Performance metrics

</td> <td width="50%">

🎨 Code Formatting

• Auto-formatting
  • black - Opinionated style
  • autopep8 - PEP8 compliance
• Configurable line length
• Consistent code style
• One-command formatting

</td> </tr> </table>

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📦 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

</details>

---

🚀 Quick Start

Step 1: Configure Your MCP Client

<details open> <summary><b>🖥️ Claude Desktop</b></summary>

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

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

💡 Tip: Replace /absolute/path/to/neurodev-mcp with your actual path

</details>

<details> <summary><b>🔧 Cline (VSCode)</b></summary>

Add to your MCP settings:

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

</details>

<details> <summary><b>🐍 Standalone Usage</b></summary>

Run the server directly:

# Using the module
python -m neurodev_mcp.server

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

</details>

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

Transport	Use Case	Best For
STDIO	Local CLI clients	Claude Desktop, Cline, local development
SSE	Web integrations	Browser apps, webhooks, remote clients

---

🛠️ 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

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📋 Requirements

Package	Version	Purpose
mcp	≥0.9.0	Model Context Protocol SDK
pylint	≥3.0.0	Code quality analysis
flake8	≥7.0.0	Style checking
mypy	≥1.7.0	Static type checking
bandit	≥1.7.5	Security scanning
radon	≥6.0.1	Complexity metrics
black	≥23.12.0	Code formatting
autopep8	≥2.0.4	PEP8 formatting
pytest	≥7.4.3	Testing framework
pytest-cov	≥4.1.0	Coverage reporting
pytest-timeout	≥2.2.0	Test timeouts

Python: 3.8 or higher

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🧪 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)

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❓ Troubleshooting

<details> <summary><b>Server not appearing in MCP client?</b></summary>

• ✅ Check that the path in config is absolute
• ✅ Ensure the Python executable path is correct
• ✅ Restart Claude Desktop or VSCode completely
• ✅ Check server logs for errors

</details>

<details> <summary><b>Import or module errors?</b></summary>

# Reinstall the package
pip install -e .

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

# Run installation tests
python test_installation.py

</details>

<details> <summary><b>Tests failing?</b></summary>

• ✅ Ensure Python 3.8+ is installed
• ✅ Activate virtual environment: source .venv/bin/activate
• ✅ Reinstall dependencies: pip install -e .
• ✅ Run: python test_installation.py to diagnose

</details>

<details> <summary><b>Performance issues?</b></summary>

• Some analyzers (pylint, mypy) can be slow on large files
• Use specific analyzers: "analyzers": ["flake8", "ast"]
• Increase timeout for large test suites
• Consider caching results (future feature)

</details>

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🤝 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

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📄 License

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

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🙏 Acknowledgments

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

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📞 Support

• 📖 Documentation: You're reading it!
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 📧 Email: team@neurodev.io

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Ready to supercharge your Python development! 🚀

Made with ❤️ by the NeuroDev Team

⭐ Star on GitHub • 🐛 Report Bug • ✨ Request Feature

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