Toolbox - MCP Server Manager
About
A web application for managing MCP servers and generating configuration files, featuring a FastAPI backend and React frontend. It automatically extracts repository information using GPT, supports YAML and JSON config generation, and provides vector-based search via Qdrant.
Details
- Author
- AlexanderOllman
- Downloads
- 137
- Categories
- Other
Jump to
- Repository management (add, view, delete)
- Automatic README information extraction via GPT
- YAML and JSON configuration generation
- Modern UI with React and Tailwind CSS
- Command-line tools for repository and config management
- Vector-based search for repositories
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Toolbox - MCP Server ManagerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Clone the repository, set up the backend (Python 3.8+, install dependencies, run python run.py on port 8020) and frontend (Node.js 18+, install dependencies, run npm run dev on port 5173). Use CLI tools like add_server.py to add Git repositories or cli.py to generate YAML configs.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"toolbox - mcp server manager": {
"Toolbox": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}
}McpServers
{
"Toolbox": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}Toolbox - MCP Server Manager
A web application for managing MCP servers and generating configuration files.
Features
- Repository management (add, view, delete)
- Automatic extraction of information from README files using GPT
- YAML and JSON configuration generation
- Modern UI with React and Tailwind CSS
- Command-line tools for repository management and config generation
- Vector-based search for repositories
Project Structure
The project is divided into two main parts:
- Backend: FastAPI server for API endpoints
- Frontend: React application with Tailwind CSS
Prerequisites
- Python 3.8+
- Node.js 18+
- Git
- OpenAI API key
- Qdrant vector database (for repository storage and search)
Getting Started
Backend Setup
1. Navigate to the backend directory:
cd backend
2. Create a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
3. Install dependencies:
pip install -r requirements.txt
4. Run the FastAPI server:
python run.py
The API will be available at http://localhost:8020.
Frontend Setup
1. Navigate to the frontend directory:
cd frontend
2. Install dependencies:
npm install
3. Start the development server:
npm run dev
The web application will be available at http://localhost:5173.
API Documentation
Once the backend is running, you can access the API documentation at:
- Swagger UI: http://localhost:8020/docs
- ReDoc: http://localhost:8020/redoc
Command-Line Tools
The application includes two command-line tools:
Add Server Tool
This tool allows you to add Git repositories to the database:
python backend/add_server.py https://github.com/username/repo.git
Optional arguments:
- --name: Custom name for the repository
- --description: Custom description for the repository
- --command: Custom command for running the repository
- --args: Custom arguments for the command
Configuration Tool
This tool provides functionality to manage the configuration:
python backend/cli.py generate-yaml
Available commands:
- generate-yaml: Generate a YAML configuration file
- -o, --output: Specify the output file path
- -p, --print: Print the YAML to stdout
- list: List all repositories in the database
Development Notes
- The OpenAI API key is hardcoded in backend/app/services/openai_service.py. In a production environment, this should be replaced with an environment variable.
- Repository data is stored in Qdrant vector database. Make sure your Qdrant instance is properly configured and backed up regularly.
- Qdrant connection parameters can be configured in the Vector Settings section of the application.
License
MIT
Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.
