Dataloop MCP

A Model Context Protocol (MCP) implementation for Dataloop AI that enables seamless integration with the Dataloop platform through a Docker-based stdio interface. MCP provides a standardized way for AI models to interact with their context and environment using standard input/output (stdio) for communication.

Features

• 🔐 Secure authentication using Dataloop API tokens
• 📚 Direct access to Dataloop documentation and Dataloop platform context
• 🐳 Docker-based deployment

Prerequisites

• Docker installed on your system
• A Dataloop AI account
• Python 3.10 or higher (for local development)

Installation

1. Get your Dataloop API Token:

   • Log in to the Dataloop platform
   • Navigate to Project > API Keys
   • Create a new API token with appropriate permissions

2. Configure MCP in Cursor:

   • Open Cursor settings
   • Navigate to MCP configuration
   • Add the following configuration:

{
  "mcpServers": {
    "dataloop-ai-mcp": {
      "command": "docker run -i --rm -e DATALOOP_API_KEY docker.io/dataloopai/mcp:latest",
      "env": {
        "DATALOOP_API_KEY": "<YOUR API TOKEN>"
      }
    }
  }
}

Usage

Once configured, you can interact with the Dataloop MCP through Cursor. The MCP provides the following functionality:

• Ask questions about Dataloop documentation
• Get real-time responses from the Dataloop knowledge base
• Access model context and environment information
• Seamless integration with your development workflow

Development

Building the Docker Image

docker build --no-cache -t docker.io/dataloopai/mcp:latest .

Running Locally

docker run -i --rm -e DATALOOP_API_KEY=<YOUR_API_TOKEN> docker.io/dataloopai/mcp:latest

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.