Tdprepview

⚠️ ALPHA SOFTWARE - DEMO USE ONLY - NOT FOR PRODUCTION

MCP server providing ML data preprocessing pipeline and model training tools for Teradata databases.

Features

• Upload datasets (iris, diabetes, wine, breast_cancer, california_housing, titanic, adult_census) to Teradata
• Create ML preprocessing pipelines with automatic feature engineering
• Generate interactive Sankey diagrams for pipeline visualization
• Train Random Forest models (classification/regression)
• Deploy models as database views using ONNX/BYOM
• Make predictions through deployed model endpoints

Installation

1. Clone repository:

   git clone <repository-url>
   cd tdprepview-mcp
   

2. Install dependencies:

   uv sync
   

3. Set up environment variables for database connection (see Configuration section below)

Configuration for Claude Desktop (macOS)

Add the following configuration to your Claude Desktop config file located at: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "tdprepview": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/YOUR_USERNAME/path/to/tdprepview-mcp",
        "run",
        "python",
        "server.py"
      ],
      "env": {
        "DB_HOST": "your-teradata-host.com",
        "DB_USER": "your_username",
        "DB_PASSWORD": "your_password"
      }
    }
  }
}

Important Notes:

1. Replace the path: Change /Users/YOUR_USERNAME/path/to/tdprepview-mcp to the actual path where you cloned this repository.

2. Set your database credentials: Replace the environment variables with your actual Teradata connection details:

   • DB_HOST: Your Teradata server hostname or IP
   • DB_USER: Your Teradata username
   • DB_PASSWORD: Your Teradata password

Available Tools

• get_dummy_data_upload - Upload datasets to Teradata with automatic indexing
• create_ml_autoprep_pipeline - Create and fit preprocessing pipelines
• save_pipeline_sankey_file - Generate interactive pipeline visualizations
• deploy_pipeline_to_database - Deploy pipelines as database views
• train_random_forest_model - Train ML models on preprocessed data
• deploy_model_to_teradata - Deploy ONNX models using BYOM
• make_predictions - Test model endpoints with sample data

Example Workflow

1. Upload dataset: "Upload the boston housing dataset to my database"
2. Create pipeline: "Create a preprocessing pipeline for this boston housing table"  
3. Generate viz: "Save a Sankey diagram for this pipeline"
4. Deploy pipeline: "Deploy the pipeline as a view "
5. Train model: "Train a classification model on it"
6. Deploy model: "Deploy this model to Teradata"
7. Test predictions: "Make some test predictions using the deployed model"

Example Execution in Claude Desktop:

Link to Chat Example using this MCP