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

# 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

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

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

{ "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

Restart Claude Desktop or reload VSCode to load the server.

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"

NeuroDev MCP supports multiple transport protocols for different use cases:

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

{ "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

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

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'] } } }) });

🔍 Comprehensive code analysis with multiple static analysis tools

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

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

🧪 Intelligent pytest test generation using AST analysis

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

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

▶️ Execute pytest tests with coverage reporting

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

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

🎨 Auto-format Python code to PEP8 standards

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

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

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

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

# Run installation tests python test_installation.py # Run examples python examples.py # Run pytest (if you add tests) pytest

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)

- ✅ Check that the path in config isabsolute
- ✅ Ensure the Python executable path is correct
- ✅ Restart Claude Desktop or VSCodecompletely
- ✅ Check server logs for errors

# Reinstall the package pip install -e . # Verify installation python -c "from neurodev_mcp import CodeAnalyzer; print('✓ OK')" # Run installation tests python test_installation.py

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

- 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)

Contributions are welcome! Here's how:
- Fork the repository
- Create a feature branch:git checkout -b feature/amazing-feature
- Make your changes
- Run tests:python test_installation.py
- Commit:git commit -m 'Add amazing feature'
- Push:git push origin feature/amazing-feature
- Open a Pull Request

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

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

- Built with theModel Context Protocol
- Powered bypylint,flake8,mypy,bandit,radon
- Testing withpytest
- Formatting withblack

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

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

A platform-agnostic code analysis library with semantic search capabilities and MCP server support.