Aindreyway MCP Server Neurolora P

🚀 MCP Server NeuroLora

NeuroLora is a platform that offers a code collection tool and a project structure report generator. This tool supports multiple programming languages and can be used via the command line or in developer mode. Its key functions include:

• Code Collection Tool: Assists users in efficiently collecting and managing code.
• Project Structure Report Generator: Automatically generates detailed project structure reports.

🚀 Quick Start

📦 Installation

1. Clone the repository.
2. Create and activate a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # For Unix systems
   # Or
   .venv\Scripts\activate  # For Windows systems
   

3. Install development dependencies:

   pip install -e ".[dev]"
   

💻 Usage Examples

Basic Usage

Run the server:

python -m mcp_server_neurolorap

Advanced Usage

Developer mode (JSON - RPC terminal interface):

python -m mcp_server_neurolorap --dev

File Storage Structure

• Create a symbolic link named .neurolora in the project root directory, pointing to the directory where the generated files are stored.
• Ensure that the file storage path is clear and easily accessible.

Custom Ignore Patterns

Create a file named .neuroloraignore in the project root directory and add custom ignore rules:

# Example ignore items
node_modules/
venv/

dist/
build/

__pycache__/
*.pyc

.vscode/
.idea/

.neurolora/

If this file does not exist, a template containing common ignore patterns will be created by default.

🔧 Technical Details

Development Process

1. Clone the repository.
2. Activate the virtual environment.
3. Install dependencies.
4. Run the server (either in normal or developer mode).

Testing and Quality Assurance

The project ensures code quality through the following measures:

# Format code
black .

# Sort imports
isort .

# Code inspection
flake8 .

# Type checking
mypy src tests

# Security scanning
bandit -r src/
safety check

CI/CD Pipeline

The project uses GitHub Actions for continuous integration and deployment:

• Run tests on Python 3.10, 3.11, and 3.12.
• Check code formatting and style.
• Perform type checking.
• Conduct security scans.
• Generate coverage reports.
• Build and validate packages.
• Upload test artifacts.

📚 Documentation

Contribution Guidelines

Contributions are welcome! Please refer to CONTRIBUTING.md for detailed information.

📄 License

This project is under the MIT License. See the LICENSE file for details.