MCP Server Neurolorap

# MCP Server Neurolorap [](https://opensource.org/licenses/MIT) [](https://github.com/aindreyway/mcp-server-neurolorap/actions/workflows/tests.yml) [](https://codecov.io/gh/aindreyway/mcp-server-neurolorap) MCP server providing tools for code analysis and documentation. <a href="https://glama.ai/mcp/servers/rg07wseeqe"><img width="380" height="200" src="https://glama.ai/mcp/servers/rg07wseeqe/badge" alt="Server Neurolorap MCP server" /></a> ## Features ### Code Collection Tool - Collect code from entire project - Collect code from specific directories or files - Collect code from multiple paths - Markdown output with syntax highlighting - Table of contents generation - Support for multiple programming languages ### Project Structure Reporter Tool - Analyze project structure and metrics - Generate detailed reports in markdown format - File size and complexity analysis - Tree-based visualization - Recommendations for code organization - Customizable ignore patterns ## Quick Overview ```sh # Using uvx (recommended) uvx mcp-server-neurolorap # Or using pip (not recommended) pip install mcp-server-neurolorap ``` You don't need to install or configure any dependencies manually. The tool will set up everything you need to analyze and document code. ## Installation **You'll need to have [UV](https://docs.astral.sh/uv/) >= 0.4.10 installed on your machine.** To install and run the server: ```sh # Install using uvx (recommended) uvx mcp-server-neurolorap # Or install using pip (not recommended) pip install mcp-server-neurolorap ``` This will automatically: - Install all required dependencies - Configure Cline integration - Set up the server for immediate use The server will be available through the MCP protocol in Cline. You can use it to analyze and document code from any project. ## Usage ### Developer Mode The server includes a developer mode with JSON-RPC terminal interface for direct interaction: ```bash # Start the server in developer mode python -m mcp_server_neurolorap --dev ``` Available commands: - `help`: Show available commands - `list_tools`: List available MCP tools - `collect <path>`: Collect code from specified path - `report [path]`: Generate project structure report - `exit`: Exit developer mode Example session: ``` > help Available commands: - help: Show this help message - list_tools: List available MCP tools - collect <path>: Collect code from specified path - report [path]: Generate project structure report - exit: Exit the terminal > list_tools ["code_collector", "project_structure_reporter"] > collect src Code collection complete! Output file: code_collection.md > report Project structure report generated: PROJECT_STRUCTURE_REPORT.md > exit Goodbye! ``` ### Through MCP Tools #### Code Collection ```python from modelcontextprotocol import use_mcp_tool # Collect code from entire project result = use_mcp_tool( "code_collector", { "input": ".", "title": "My Project" } ) # Collect code from specific directory result = use_mcp_tool( "code_collector", { "input": "./src", "title": "Source Code" } ) # Collect code from multiple paths result = use_mcp_tool( "code_collector", { "input": ["./src", "./tests"], "title": "Project Files" } ) ``` #### Project Structure Analysis ```python # Generate project structure report result = use_mcp_tool( "project_structure_reporter", { "output_filename": "PROJECT_STRUCTURE_REPORT.md" } ) # Analyze specific directory with custom ignore patterns result = use_mcp_tool( "project_structure_reporter", { "output_filename": "src_structure.md", "ignore_patterns": ["*.pyc", "__pycache__"] } ) ``` ### File Storage The server uses a structured approach to file storage: 1. All generated files are stored in `~/.mcp-docs/<project-name>/` 2. A `.neurolora` symlink is created in your project root pointing to this directory This ensures: - Clean project structure - Consistent file organization - Easy access to generated files - Support for multiple projects - Reliable file synchronization across different OS environments - Fast file visibility in IDEs and file explorers ### Customizing Ignore Patterns Create a `.neuroloraignore` file in your project root to customize which files are ignored: ```gitignore # Dependencies node_modules/ venv/ # Build dist/ build/ # Cache __pycache__/ *.pyc # IDE .vscode/ .idea/ # Generated files .neurolora/ ``` If no `.neuroloraignore` file exists, a default one will be created with common ignore patterns. ## Development 1. Clone the repository 2. Create and activate virtual environment: ```sh python -m venv .venv source .venv/bin/activate # On Unix # or .venv\Scripts\activate # On Windows ``` 3. Install development dependencies: ```sh pip install -e ".[dev]" ``` 4. Run the server: ```sh # Normal mode (MCP server with stdio transport) python -m mcp_server_neurolorap # Developer mode (JSON-RPC terminal interface) python -m mcp_server_neurolorap --dev ``` ### Testing The project maintains high quality standards through automated testing and continuous integration: - Comprehensive test suite with over 80% code coverage - Automated testing on Python 3.10, 3.11, and 3.12 - Continuous integration through GitHub Actions - Regular security scans and dependency checks For development and testing details, see PROJECT_SUMMARY.md. ### Code Quality The project maintains high code quality standards through various tools: ```sh # Format code black . # Sort imports isort . # Lint code flake8 . # Type check mypy src tests # Security check bandit -r src/ safety check ``` All these checks are run automatically on pull requests through GitHub Actions. ### CI/CD Pipeline The project uses GitHub Actions for continuous integration and deployment: - Runs tests on Python 3.10, 3.11, and 3.12 - Checks code formatting and style - Performs type checking - Runs security scans - Generates coverage reports - Builds and validates package - Uploads test artifacts The pipeline must pass before merging any changes. ## Contributing We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. ## License MIT License. See LICENSE file for details.