MCP Server Neurolorap

MCP server providing tools for code analysis and documentation.

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

# 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 >= 0.4.10 installed on your machine.

To install and run the server:

# 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:

# 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

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

# 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:

# 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:

python -m venv .venv
source .venv/bin/activate  # On Unix
# or
.venv\Scripts\activate  # On Windows

3. Install development dependencies:

pip install -e ".[dev]"

4. Run the server:

# 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:

# 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 for guidelines.

License

MIT License. See LICENSE file for details.