ML Engineer Pack

Train and deploy models: HuggingFace → LlamaIndex → Snowflake data pipelines

⏱️ Setup time: 40 minutesDifficulty: advancedRead full guide

Servers Included

Configuration

Copy the config for your MCP client. Replace your_api_key with your RouterMCP API key.

{
  "mcpServers": {
    "routermcp": {
      "url": "https://your-project.workers.dev/v1/mcp/ml-engineer-pack/request",
      "apiKey": "your_api_key_here"
    }
  }
}

Quick Setup

  1. Create a project in your RouterMCP dashboard
  2. Add servers for each tool in this pack (see list above)
  3. Generate an API key in API Keys
  4. Copy the config above and paste into your MCP client
  5. Test the connection with the cURL snippet

Required Environment Variables

Most servers require API keys or tokens. Check each server's documentation for details.

# Example .env file
HUGGINGFACE_API_KEY=your_huggingface_key
LLAMAINDEX_API_KEY=your_llamaindex_key
SNOWFLAKE_API_KEY=your_snowflake_key

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