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# 🧠 NeuroDev MCP Server
### *Intelligent Code Analysis, Test Generation & Execution*
[](https://www.python.org/downloads/)
[](https://modelcontextprotocol.io/)
[](https://opensource.org/licenses/MIT)
[](./test_installation.py)
**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](#-features) • [Installation](#-installation) • [Quick Start](#-quick-start) • [Tools](#-available-tools) • [Examples](#-usage-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>
---
## 📦 Installation
### **Quick Install**
\`\`\`bash
```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`:
```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:
```json
{
"neurodev-mcp": {
"command": "python",
"args": ["-m", "neurodev_mcp.server"]
}
}
```
</details>
<details>
<summary><b>🐍 Standalone Usage</b></summary>
Run the server directly:
```bash
# 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:
```bash
# Default STDIO transport
neurodev-mcp
# Or explicitly specify STDIO
neurodev-mcp --transport stdio
```
**Configuration (Claude Desktop):**
```json
{
"mcpServers": {
"neurodev-mcp": {
"command": "neurodev-mcp",
"args": ["--transport", "stdio"]
}
}
}
```
### **SSE (Server-Sent Events) - Web Integration**
For web-based integrations and HTTP streaming:
```bash
# 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:**
```javascript
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:**
```json
{
"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:**
```json
{
"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:**
```json
{
"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:**
```json
{
"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**
```bash
# Run installation tests
python test_installation.py
# Run examples
python examples.py
# Run pytest (if you add tests)
pytest
```
### **Using as a Library**
```python
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
<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>
```bash
# 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>
---
## 🤝 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](LICENSE) file for details.
---
## 🙏 Acknowledgments
- Built with the [Model Context Protocol](https://modelcontextprotocol.io/)
- Powered by [pylint](https://pylint.org/), [flake8](https://flake8.pycqa.org/), [mypy](http://mypy-lang.org/), [bandit](https://bandit.readthedocs.io/), [radon](https://radon.readthedocs.io/)
- Testing with [pytest](https://pytest.org/)
- Formatting with [black](https://black.readthedocs.io/)
---
## 📞 Support
- 📖 **Documentation**: You're reading it!
- 🐛 **Issues**: [GitHub Issues](https://github.com/ravikant1918/neurodev-mcp/issues)
- 💬 **Discussions**: [GitHub Discussions](https://github.com/ravikant1918/neurodev-mcp/discussions)
- 📧 **Email**: team@neurodev.io
---
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### **Ready to supercharge your Python development!** 🚀
Made with ❤️ by the NeuroDev Team
[⭐ Star on GitHub](https://github.com/ravikant1918/neurodev-mcp) • [🐛 Report Bug](https://github.com/ravikant1918/neurodev-mcp/issues) • [✨ Request Feature](https://github.com/ravikant1918/neurodev-mcp/issues)
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