Toolbox - MCP Server Manager
@AlexanderOllman
About Toolbox - MCP Server Manager
Agnostic MCP server addition for FastAgent
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"Toolbox": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}Tools
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We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is Toolbox - MCP Server Manager?
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.
How to use Toolbox - MCP Server Manager?
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.
Key features of Toolbox - MCP Server Manager
- 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
Use cases of Toolbox - MCP Server Manager
- Centralizing and managing multiple MCP server repositories
- Automatically generating MCP configuration files from repository metadata
- Searching through MCP server repositories using semantic similarity
- Maintaining a local catalog of MCP servers with automatic documentation extraction
FAQ from Toolbox - MCP Server Manager
What are the main dependencies to run Toolbox - MCP Server Manager?
Python 3.8+, Node.js 18+, Git, an OpenAI API key, and a Qdrant vector database instance.
Where is repository data stored and how is it managed?
Repository data is stored in a Qdrant vector database. The connection parameters can be configured in the Vector Settings section of the application.
What CLI tools are available and how do I use them?
Two tools: add_server.py (adds Git repositories to the database) and cli.py (generates YAML configs, lists repositories). Run them from the backend directory with appropriate arguments.
Is the OpenAI API key handled securely?
The README notes that the API key is currently hardcoded in backend/app/services/openai_service.py and recommends using an environment variable in production.
What transports and authentication are used?
The API is served via HTTP on port 8020, with Swagger UI and ReDoc documentation. No authentication mechanism is mentioned in the README.
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