marc-shade/synthetic-data-mcp

Synthetic Data MCP Server

Synthetic data generation for training and testing.

Part of the Agentic System - a 24/7 autonomous AI framework with persistent memory.

Enterprise-grade Model Context Protocol (MCP) server for generating privacy-compliant synthetic datasets. Built for regulated industries requiring HIPAA, PCI DSS, SOX, and GDPR compliance with multiple LLM provider support.

Features

Core Capabilities

• Privacy-First Local Inference: Ollama integration for 100% local data generation
• Domain-Specific Generation: Specialized synthetic data for healthcare and finance
• Privacy Protection: Differential privacy, k-anonymity, l-diversity
• PII Safety Guarantee: Never retains or outputs original personal data
• Compliance Validation: HIPAA, PCI DSS, SOX, GDPR compliance checking
• Statistical Fidelity: Advanced validation to ensure data utility
• Audit Trail: Comprehensive logging for regulatory compliance
• Multi-Provider Support: Ollama (default), OpenAI, Anthropic, Google, OpenRouter

LLM Provider Support (2025 Models)

• OpenAI: GPT-5, GPT-5 Mini/Nano, GPT-4o
• Anthropic: Claude Opus 4.1, Claude Sonnet 4 (1M context), Claude 3.5 series
• Google: Gemini 2.5 Pro/Flash/Flash-Lite (1M+ context, multimodal)
• Local Models: Dynamic Ollama integration (Llama 3.3, Qwen 2.5/3, DeepSeek-R1, Mistral Small 3)
• Smart Routing: Automatic provider selection with cost optimization
• Fallback: Multi-tier fallback with local model support

Technology Stack (2025 Latest)

• FastAPI 0.116+: High-performance async web framework
• FastMCP: High-performance MCP server implementation
• Pydantic 2.11+: Type-safe data validation with enhanced performance
• SQLAlchemy 2.0+: Modern async ORM with type safety
• DSPy: Language model programming framework for intelligent data generation
• NumPy 2.3+ & Pandas 2.3+: Advanced data processing capabilities
• Redis & DiskCache: Multi-tier caching for cost optimization
• Rich: Beautiful terminal interfaces and progress indicators

Enterprise Benefits

• Privacy-First: Generate synthetic data without exposing sensitive information
• Compliance-Ready: Built-in validation for HIPAA, PCI DSS, SOX, and GDPR
• Multi-Provider: Support for cloud APIs and local inference
• Production-Scale: High-performance generation for enterprise data volumes
• Zero Vendor Lock-in: Switch between providers seamlessly
• Cost Control: Use local models for unlimited generation

Healthcare Use Cases

• Patient record synthesis with HIPAA Safe Harbor compliance
• Clinical trial data generation for FDA submissions
• Medical research datasets without PHI exposure
• Drug discovery data augmentation
• Healthcare analytics and ML model training
• EHR system testing and validation

Finance Use Cases

• Transaction pattern modeling for fraud detection
• Credit risk assessment dataset generation
• Regulatory stress testing data (Basel III, Dodd-Frank)
• PCI DSS compliant payment data synthesis
• Trading algorithm development and backtesting
• Financial reporting system validation

Installation

Production Installation

pip install synthetic-data-mcp

Development Installation

git clone https://github.com/marc-shade/synthetic-data-mcp
cd synthetic-data-mcp
pip install -e ".[dev,healthcare,finance]"

Quick Start

1. Configure LLM Provider

Choose your preferred provider:

OpenAI (Recommended for Production)

export OPENAI_API_KEY="sk-your-key-here"

Anthropic Claude

export ANTHROPIC_API_KEY="sk-ant-your-key-here"

Google Gemini

export GOOGLE_API_KEY="your-key-here"

OpenRouter (Access to 100+ Models)

export OPENROUTER_API_KEY="sk-or-your-key-here"
export OPENROUTER_MODEL="meta-llama/llama-3.1-8b-instruct"

Local Models (Ollama) - Privacy-First (DEFAULT)

# Install Ollama first: https://ollama.ai
ollama pull mistral-small:latest  # Or any preferred model
export OLLAMA_BASE_URL="http://localhost:11434"
export OLLAMA_MODEL="mistral-small:latest"

# The system automatically detects and uses Ollama if available
# No API keys required for local inference!

2. Start the MCP Server

synthetic-data-mcp serve --port 3000

3. Add to Claude Desktop Configuration

{
  "mcpServers": {
    "synthetic-data": {
      "command": "python",
      "args": ["-m", "synthetic_data_mcp.server"],
      "env": {
        "OPENAI_API_KEY": "your-api-key"
      }
    }
  }
}

4. Generate Synthetic Data

# Using the MCP client
result = await client.call_tool(
    "generate_synthetic_dataset",
    {
        "domain": "healthcare",
        "dataset_type": "patient_records",
        "record_count": 10000,
        "privacy_level": "high",
        "compliance_frameworks": ["hipaa"],
        "output_format": "json"
    }
)

Provider Configuration

Priority-Based Provider Selection

The system automatically selects the best available provider:

1. Local Models (Ollama) - Highest privacy, no API costs
2. OpenAI - Best performance and reliability
3. Anthropic Claude - Excellent reasoning capabilities
4. Google Gemini - Fast and cost-effective
5. OpenRouter - Access to open source models
6. Fallback Mock - Testing and development

Provider-Specific Configuration

OpenAI Configuration

# Environment variables
OPENAI_API_KEY="sk-your-key-here"
OPENAI_MODEL="gpt-4"  # or gpt-4-turbo, gpt-3.5-turbo
OPENAI_TEMPERATURE="0.7"
OPENAI_MAX_TOKENS="2000"

Anthropic Configuration

# Environment variables
ANTHROPIC_API_KEY="sk-ant-your-key-here"
ANTHROPIC_MODEL="claude-3-opus-20240229"  # or claude-3-sonnet, claude-3-haiku
ANTHROPIC_MAX_TOKENS="2000"

Local Ollama Configuration

# Environment variables
OLLAMA_BASE_URL="http://localhost:11434"
OLLAMA_MODEL="llama3.1:8b"  # or any installed model

# Supported local models:
# - llama3.1:8b, llama3.1:70b
# - mistral:7b, mixtral:8x7b
# - qwen2:7b, deepseek-coder:6.7b
# - and 20+ more models

Available MCP Tools

generate_synthetic_dataset

Generate domain-specific synthetic datasets with compliance validation.

Parameters:

• domain: Healthcare, finance, or custom
• dataset_type: Patient records, transactions, clinical trials, etc.
• record_count: Number of synthetic records to generate
• privacy_level: Privacy protection level (low/medium/high/maximum)
• compliance_frameworks: Required compliance validations
• output_format: JSON, CSV, Parquet, or database export
• provider: Override automatic provider selection

validate_dataset_compliance

Validate existing datasets against regulatory requirements.

analyze_privacy_risk

Comprehensive privacy risk assessment for datasets.

generate_domain_schema

Create Pydantic schemas for domain-specific data structures.

benchmark_synthetic_data

Performance and utility benchmarking against real data.

Compliance Frameworks

Healthcare Compliance

• HIPAA Safe Harbor: Automatic validation of 18 identifiers
• HIPAA Expert Determination: Statistical disclosure control
• FDA Guidance: Synthetic clinical data for submissions
• GDPR: Healthcare data processing compliance
• HITECH: Security and breach notification

Finance Compliance

• PCI DSS: Payment card industry data security
• SOX: Sarbanes-Oxley internal controls
• Basel III: Banking regulatory framework
• MiFID II: Markets in Financial Instruments Directive
• Dodd-Frank: Financial reform regulations

Privacy Protection

Core Privacy Features

• Differential Privacy: Configurable ε values (0.1-1.0)
• Statistical Disclosure Control: k-anonymity, l-diversity, t-closeness
• Synthetic Data Indistinguishability: Provable privacy guarantees
• Re-identification Risk Assessment: Continuous monitoring
• Privacy Budget Management: Automatic composition tracking

PII Protection Guarantee

• NO Data Retention: Original personal data is NEVER stored
• Automatic PII Detection: Identifies names, emails, SSNs, phones, addresses, credit cards
• Complete Anonymization: All PII is anonymized before pattern learning
• Statistical Learning Only: Only learns distributions, means, and frequencies
• 100% Synthetic Output: Generated data is completely fake

Credit Card Safety

• Test Card Numbers Only: Uses official test cards (4242-4242-4242-4242, etc.)
• Provider Support: Visa, Mastercard, AmEx, Discover, and more
• Configurable Providers: Specify provider or use weighted distribution
• Never Real Cards: Original credit card numbers are never retained or output

Example usage with credit card provider selection:

# Use specific provider test cards
result = await pipeline.ingest(
    source=data,
    credit_card_provider='visa'  # Uses Visa test cards
)

# Or let system use mixed providers (default)
result = await pipeline.ingest(
    source=data  # Automatically uses weighted distribution
)

Performance & Quality

• Statistical Fidelity: 95%+ correlation preservation
• Privacy Preservation: <1% re-identification risk
• Utility Preservation: >90% ML model performance
• Compliance Rate: 100% regulatory framework adherence
• Generation Speed: 1,000-10,000 records/second (provider dependent)

Provider Performance Comparison

Testing

# Run all tests
pytest

# Run compliance tests only
pytest -m compliance

# Run privacy tests
pytest -m privacy

# Run with coverage
pytest --cov=synthetic_data_mcp --cov-report=html

# Test specific provider
OPENAI_API_KEY=sk-test pytest -m integration

Deployment

Docker Deployment

docker build -t synthetic-data-mcp .
docker run -p 3000:3000 \
  -e OPENAI_API_KEY=your-key \
  synthetic-data-mcp

Kubernetes Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: synthetic-data-mcp
spec:
  replicas: 3
  selector:
    matchLabels:
      app: synthetic-data-mcp
  template:
    metadata:
      labels:
        app: synthetic-data-mcp
    spec:
      containers:
      - name: synthetic-data-mcp
        image: synthetic-data-mcp:latest
        ports:
        - containerPort: 3000
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: llm-secrets
              key: openai-key

🔧 Development

Code Quality

# Format code
black .
isort .

# Run linting
flake8 src tests

# Type checking
mypy src

Adding New Providers

1. Create provider module in src/synthetic_data_mcp/providers/
2. Implement DSPy LM interface
3. Add configuration in core/generator.py
4. Add tests in tests/test_providers.py

Examples

Healthcare Example

import asyncio
from synthetic_data_mcp import SyntheticDataGenerator

async def generate_patients():
    generator = SyntheticDataGenerator()
    
    result = await generator.generate_dataset(
        domain="healthcare",
        dataset_type="patient_records",
        record_count=1000,
        privacy_level="high",
        compliance_frameworks=["hipaa"]
    )
    
    print(f"Generated {len(result['dataset'])} patient records")
    return result

# Run the example
asyncio.run(generate_patients())

Finance Example

async def generate_transactions():
    generator = SyntheticDataGenerator()
    
    result = await generator.generate_dataset(
        domain="finance",
        dataset_type="transactions",
        record_count=50000,
        privacy_level="high",
        compliance_frameworks=["pci_dss"]
    )
    
    print(f"Generated {len(result['dataset'])} transactions")
    return result

Contributing

We welcome contributions! Please see our for details.

Development Setup

git clone https://github.com/marc-shade/synthetic-data-mcp
cd synthetic-data-mcp
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,healthcare,finance]"
pre-commit install

License

MIT License - see file for details.

Support

• GitHub Issues
• GitHub Discussions
• Email: support@2acrestudios.com

Related Projects

• Model Context Protocol (MCP)
• DSPy Framework
• Ollama - Local LLM inference
• OpenRouter - Access to 100+ models

Built with ❤️ for enterprise developers who need compliant, privacy-preserving synthetic data generation.

Part of the MCP Ecosystem

This server integrates with other MCP servers for comprehensive AGI capabilities:

See agentic-system-oss for the complete framework.