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

Install uv from Astral or the GitHub README
Install Python using uv python install 3.10
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

{
  "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
    }
  }
}

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"
        }
    }]
)

Synthetic Data MCP Server