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:
```bash
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
```
2. Install Node.js 18:
```bash
brew install node@18
echo 'export PATH="/opt/homebrew/opt/node@18/bin:$PATH"' >> ~/.zshrc
source ~/.zshrc
```
#### Windows
1. Download Node.js 18 LTS from [nodejs.org](https://nodejs.org/)
2. Run the installer
3. Open a new terminal to apply changes
#### Linux (Ubuntu/Debian)
```bash
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:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
2. Install uvx:
```bash
uv pip install uvx
```
### Step 3: Verify Installation
Run these commands to verify everything is installed:
```bash
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:
```json
{
"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:
```json
{
"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:
```json
{
"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:
```json
{
"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**
- GitHub: [@aindreyway](https://github.com/aindreyway)
## ⭐️ Support
Give a ⭐️ if this project helped you!
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap: analyze_code for code analysis, collect_code for file aggregation, create_github_issues for issue creation, and install_base_servers for server installation. The descriptions clearly differentiate their functions, eliminating any ambiguity.
All tool names follow a consistent verb_noun pattern (e.g., analyze_code, collect_code, create_github_issues, install_base_servers). The naming is uniform and predictable, using snake_case throughout with clear action-object pairs.
With only 4 tools, the set feels thin for a server named 'mcp-neurolora', which suggests a broader scope related to code analysis or AI workflows. While the tools cover specific tasks, the count is borderline low, potentially leaving gaps in functionality for the implied domain.
The tool set has significant gaps for a code analysis or AI workflow server. It lacks core operations like retrieving or updating issues, managing analysis results, or handling configurations beyond installation. This incomplete surface will likely cause agent failures in extended workflows.