mcp-neurolora

MCP Neurolora

An intelligent MCP server that provides tools for code analysis using OpenAI API, code collection, and documentation generation.

🚀 Installation Guide

Don't worry if you don't have anything installed yet! Just follow these steps or ask your assistant to help you with the installation.

Step 1: Install Node.js

macOS

  1. Install Homebrew if not installed:
    /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
  2. Install Node.js 18:
    brew install node@18 echo 'export PATH="/opt/homebrew/opt/node@18/bin:$PATH"' >> ~/.zshrc source ~/.zshrc

Windows

  1. Download Node.js 18 LTS from nodejs.org
  2. Run the installer
  3. Open a new terminal to apply changes

Linux (Ubuntu/Debian)

curl -fsSL https://deb.nodesource.com/setup_18.x | sudo -E bash - sudo apt-get install -y nodejs

Step 2: Install uv and uvx

All Operating Systems

  1. Install uv:
    curl -LsSf https://astral.sh/uv/install.sh | sh
  2. Install uvx:
    uv pip install uvx

Step 3: Verify Installation

Run these commands to verify everything is installed:

node --version # Should show v18.x.x npm --version # Should show 9.x.x or higher uv --version # Should show uv installed uvx --version # Should show uvx installed

Step 4: Configure MCP Server

Your assistant will help you:

  1. Find your Cline settings file:
    • VSCode: ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
    • Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows VSCode: %APPDATA%/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
    • Windows Claude: %APPDATA%/Claude/claude_desktop_config.json
  2. Add this configuration:
    { "mcpServers": { "aindreyway-mcp-neurolora": { "command": "npx", "args": ["-y", "@aindreyway/mcp-neurolora@latest"], "env": { "NODE_OPTIONS": "--max-old-space-size=256", "OPENAI_API_KEY": "your_api_key_here" } } } }

Step 5: Install Base Servers

Simply ask your assistant: "Please install the base MCP servers for my environment"

Your assistant will:

  1. Find your settings file
  2. Run the install_base_servers tool
  3. Configure all necessary servers automatically

After the installation is complete:

  1. Close VSCode completely (Cmd+Q on macOS, Alt+F4 on Windows)
  2. Reopen VSCode
  3. The new servers will be ready to use

Important: A complete restart of VSCode is required after installing the base servers for them to be properly initialized.

Note: This server uses npx for direct npm package execution, which is optimal for Node.js/TypeScript MCP servers, providing seamless integration with the npm ecosystem and TypeScript tooling.

Base MCP Servers

The following base servers will be automatically installed and configured:

  • fetch: Basic HTTP request functionality for accessing web resources
  • puppeteer: Browser automation capabilities for web interaction and testing
  • sequential-thinking: Advanced problem-solving tools for complex tasks
  • github: GitHub integration features for repository management
  • git: Git operations support for version control
  • shell: Basic shell command execution with common commands:
    • ls: List directory contents
    • cat: Display file contents
    • pwd: Print working directory
    • grep: Search text patterns
    • wc: Count words, lines, characters
    • touch: Create empty files
    • find: Search for files

🎯 What Your Assistant Can Do

Ask your assistant to:

  • "Analyze my code and suggest improvements"
  • "Install base MCP servers for my environment"
  • "Collect code from my project directory"
  • "Create documentation for my codebase"
  • "Generate a markdown file with all my code"

🛠 Available Tools

analyze_code

Analyzes code using OpenAI API and generates detailed feedback with improvement suggestions.

Parameters:

  • codePath (required): Path to the code file or directory to analyze

Example usage:

{ "codePath": "/path/to/your/code.ts" }

The tool will:

  1. Analyze your code using OpenAI API
  2. Generate detailed feedback with:
    • Issues and recommendations
    • Best practices violations
    • Impact analysis
    • Steps to fix
  3. Create two output files in your project:
    • LAST_RESPONSE_OPENAI.txt - Human-readable analysis
    • LAST_RESPONSE_OPENAI_GITHUB_FORMAT.json - Structured data for GitHub issues

Note: Requires OpenAI API key in environment configuration

collect_code

Collects all code from a directory into a single markdown file with syntax highlighting and navigation.

Parameters:

  • directory (required): Directory path to collect code from
  • outputPath (optional): Path where to save the output markdown file
  • ignorePatterns (optional): Array of patterns to ignore (similar to .gitignore)

Example usage:

{ "directory": "/path/to/project/src", "outputPath": "/path/to/project/src/FULL_CODE_SRC_2024-12-20.md", "ignorePatterns": ["*.log", "temp/", "__pycache__", "*.pyc", ".git"] }

install_base_servers

Installs base MCP servers to your configuration file.

Parameters:

  • configPath (required): Path to the MCP settings configuration file

Example usage:

{ "configPath": "/path/to/cline_mcp_settings.json" }

🔧 Features

The server provides:

  • Code Analysis:
    • OpenAI API integration
    • Structured feedback
    • Best practices recommendations
    • GitHub issues generation
  • Code Collection:
    • Directory traversal
    • Syntax highlighting
    • Navigation generation
    • Pattern-based filtering
  • Base Server Management:
    • Automatic installation
    • Configuration handling
    • Version management

📄 License

MIT License - feel free to use this in your projects!

👤 Author

Aindreyway

⭐️ Support

Give a ⭐️ if this project helped you!