We are going to implement an MCP server to orchestrate the Synthetic Data Vault (SDV) for completely local synthetic data generation. Agents will be able to connect to discover tools and then create, statistically evaluate, and visualize synthetic datasets based on our real-world tabular data.

We use:

SDV for synthetic data generation of tabular data
Cursor (MCP Host)

Set Up

Run these commands in project root

Install Dependencies

uv sync

Use MCP Server

Run the MCP server with the created configuration file as mcp.json either globally or in the current project directory. Here's the code of configuring MCP globally to run the server:

{
  "mcpServers": {
    "sdv_mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/akshay/Eigen/ai-engineering-hub/sdv-mcp",
        "run",
        "--with",
        "mcp",
        "server.py"
      ]
    }
  }
}

Get a FREE Data Science eBook 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. Subscribe now!

Contribution

Contributions are welcome! Feel free to fork this repository and submit pull requests with your improvements.

README.md

SDV MCP Server

Set Up

Install Dependencies

Use MCP Server

📬 Stay Updated with Our Newsletter!

Contribution