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ravikant1918

neurodev-mcp

by ravikant1918
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Python

🧠 NeuroDev MCP Server

Intelligent Code Analysis, Test Generation & Execution

Python 3.8+ MCP License: MIT Tests

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

✨ Features

🔍 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

🧪 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

▶️ Test Execution

  • Comprehensive Testing

    • Isolated environment

    • Coverage reporting

    • Line-by-line analysis

    • Timeout protection

  • Detailed pass/fail results

  • Performance metrics

🎨 Code Formatting

  • Auto-formatting

    • black - Opinionated style

    • autopep8 - PEP8 compliance

  • Configurable line length

  • Consistent code style

  • One-command formatting

📦 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

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

Add to your MCP settings:

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

Run the server directly:

# 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

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.

🔍 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.

🧪 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.

▶️ 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.

🎨 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

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

🧪 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

  • ✅ 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

# 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.py to 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)

🤝 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

📞 Support

Ready to supercharge your Python development! 🚀

Made with ❤️ by the NeuroDev Team

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

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

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