⚠️ DEPRECATION NOTICE: This server is deprecated and will no longer receive updates. The core functionality (generating synthetic data schemas and pandas code) is achievable natively by modern AI assistants without requiring an MCP server. For S3 uploads, use the S3 MCP Server instead.

Synthetic Data MCP Server

A Model Context Protocol (MCP) server for generating, validating, and managing synthetic data.

Overview

This MCP server provides tools for generating synthetic data based on business descriptions, executing pandas code safely, validating data structures, and loading data to storage systems like S3.

Features

• Business-Driven Generation: Generate synthetic data instructions based on business descriptions
• Data Generation Instructions: Generate structured data generation instructions from business descriptions
• Safe Pandas Code Execution: Run pandas code in a restricted environment with automatic DataFrame detection
• JSON Lines Validation: Validate and convert JSON Lines data to CSV format
• Data Validation: Validate data structure, referential integrity, and save as CSV files
• Referential Integrity Checking: Validate relationships between tables
• Data Quality Assessment: Identify potential issues in data models (3NF validation)
• Storage Integration: Load data to various storage targets (S3) with support for:
  • Multiple file formats (CSV, JSON, Parquet)
  • Partitioning options
  • Storage class configuration
  • Encryption settings

Prerequisites

1. Install uv from Astral or the GitHub README
2. Install Python using uv python install 3.10
3. Set up AWS credentials with access to AWS services
   • You need an AWS account with appropriate permissions
   • Configure AWS credentials with aws configure or environment variables

Installation

Kiro	Cursor	VS Code

{
  "mcpServers": {
    "awslabs.syntheticdata-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.syntheticdata-mcp-server"],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR",
        "AWS_PROFILE": "your-aws-profile",
        "AWS_REGION": "us-east-1"
      },
      "autoApprove": [],
      "disabled": false
    }
  }
}

Windows Installation

For Windows users, the MCP server configuration format is slightly different:

{
  "mcpServers": {
    "awslabs.syntheticdata-mcp-server": {
      "disabled": false,
      "timeout": 60,
      "type": "stdio",
      "command": "uv",
      "args": [
        "tool",
        "run",
        "--from",
        "awslabs.syntheticdata-mcp-server@latest",
        "awslabs.syntheticdata-mcp-server.exe"
      ],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR",
        "AWS_PROFILE": "your-aws-profile",
        "AWS_REGION": "us-east-1"
      }
    }
  }
}

NOTE: Your credentials will need to be kept refreshed from your host

AWS Authentication

The MCP server uses the AWS profile specified in the AWS_PROFILE environment variable. If not provided, it defaults to the "default" profile in your AWS configuration file.

"env": {
  "AWS_PROFILE": "your-aws-profile"
}

Usage

Getting Data Generation Instructions

response = await server.get_data_gen_instructions(
    business_description="An e-commerce platform with customers, orders, and products"
)

Executing Pandas Code

response = await server.execute_pandas_code(
    code="your_pandas_code_here",
    workspace_dir="/path/to/workspace",
    output_dir="data"
)

Validating and Saving Data

response = await server.validate_and_save_data(
    data={
        "customers": [{"id": 1, "name": "John"}],
        "orders": [{"id": 101, "customer_id": 1}]
    },
    workspace_dir="/path/to/workspace",
    output_dir="data"
)

Loading to Storage

response = await server.load_to_storage(
    data={
        "customers": [{"id": 1, "name": "John"}]
    },
    targets=[{
        "type": "s3",
        "config": {
            "bucket": "my-bucket",
            "prefix": "data/",
            "format": "parquet"
        }
    }]
)