Amazon DataZone MCP Server

A high-performance Model Context Protocol (MCP) server that provides seamless integration with Amazon DataZone services. This server enables AI assistants and applications to interact with Amazon DataZone APIs through a standardized interface.

Features

• Complete Amazon DataZone API Coverage: Access all major DataZone operations
• Modular Architecture: Well-organized, maintainable code structure
• Type Safety: Full TypeScript-style type hints for Python
• Comprehensive Error Handling: Detailed error messages and proper exception handling
• Production Ready: Robust logging, validation, and configuration management

Supported Operations

Module	Operations
Domain Management	Create domains, manage domain units, search, policy grants
Project Management	Create/manage projects, project profiles, memberships
Data Management	Assets, listings, subscriptions, form types, data sources
Glossary	Business glossaries, glossary terms
Environment	Environments, connections, blueprints

Installation

Prerequisites

• Python 3.10 or higher
• AWS credentials configured
• An active Amazon DataZone domain

Install from PyPI

pip install datazone-mcp-server

Install from Source

git clone https://github.com/awslabs/amazon-datazone-mcp-server.git
cd datazone-mcp-server
pip install -e .

Quick Start

1. Configure AWS Credentials

# Using AWS CLI
aws configure

# Or set environment variables
export AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key
export AWS_DEFAULT_REGION=us-east-1

2. Start the MCP Server

python -m datazone_mcp_server.server

3. Use with an MCP Client

import asyncio
from mcp import create_client

async def main():
    # Connect to the DataZone MCP server
    client = await create_client("stdio", ["python", "-m", "datazone_mcp_server.server"])
    
    # List available tools
    tools = await client.list_tools()
    print(f"Available tools: {len(tools.tools)}")
    
    # Call a DataZone operation
    result = await client.call_tool("get_domain", {
        "identifier": "dzd_your_domain_id"
    })
    print(result.content)

asyncio.run(main())

Usage Examples

Creating a DataZone Domain

# Create a new DataZone domain
domain = await client.call_tool("create_domain", {
    "name": "MyDataDomain",
    "domain_execution_role": "arn:aws:iam::123456789012:role/DataZoneExecutionRole",
    "service_role": "arn:aws:iam::123456789012:role/DataZoneServiceRole",
    "description": "My data governance domain"
})

Managing Projects

# Create a project
project = await client.call_tool("create_project", {
    "domain_identifier": "dzd_abc123",
    "name": "Analytics Project",
    "description": "Project for analytics workloads"
})

# List projects
projects = await client.call_tool("list_projects", {
    "domain_identifier": "dzd_abc123"
})

Working with Assets

# Create an asset
asset = await client.call_tool("create_asset", {
    "domain_identifier": "dzd_abc123",
    "name": "Customer Data",
    "type_identifier": "amazon.datazone.RelationalTable",
    "owning_project_identifier": "prj_xyz789"
})

# Publish the asset
published = await client.call_tool("publish_asset", {
    "domain_identifier": "dzd_abc123",
    "asset_identifier": asset["id"]
})

Architecture

datazone-mcp-server/
├── src/datazone_mcp_server/
│   ├── server.py              # Main MCP server entry point
│   ├── tools/                 # Tool modules
│   │   ├── common.py          # Shared utilities
│   │   ├── domain_management.py
│   │   ├── project_management.py
│   │   ├── data_management.py
│   │   ├── glossary.py
│   │   └── environment.py
│   └── __init__.py
├── tests/                     # Test suite
├── examples/                  # Usage examples
└── docs/                      # Documentation

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/awslabs/amazon-datazone-mcp-server.git
cd datazone-mcp-server

# Install with development dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=datazone_mcp_server

# Run specific test file
pytest tests/test_domain_management.py

Code Quality

# Format code
black src tests
isort src tests

# Type checking
mypy src

# Linting
flake8 src tests

Available Tools

The Amazon DataZone MCP server provides 38 tools organized into 5 categories:

Domain Management

• get_domain - Retrieve domain information
• create_domain - Create a new domain
• list_domain_units - List domain units
• create_domain_unit - Create domain unit
• list_domains - List domains
• add_entity_owner - Add entity ownership
• add_policy_grant - Grant policies
• search - Search across DataZone
• search_types - Search typs across DataZone
• get_user_profile - Get user profile
• search_user_profiles - Search user profiles
• search_group_profiles - Search group profiles

Project Management

• create_project - Create new project
• get_project - Get project details
• list_projects - List all projects
• create_project_membership - Add project members
• list_project_profiles - List project profiles
• create_project_profile - Create project profile
• get_project_profile - Get project profile
• list_project_memberships - List project memberships

Glossary Management

• create_glossary - Create business glossary
• create_glossary_term - Create glossary term
• get_glossary - Get glossary details
• get_glossary_term - Get term details

Data Management

• get_asset - Retrieve asset information
• create_asset - Create new asset
• publish_asset - Publish asset to catalog
• get_listing - Get asset listing
• search_listings - Search published assets
• create_data_source - Create data source
• get_data_source - Get data source
• start_data_source_run - Start data source run
• create_subscription_request - Request data subscription
• accept_subscription_request - Accept subscription
• get_form_type - Get metadata form type
• create_form_type - Create metadata form type
• get_subscription - Get subscription
• list_data_sources - List data sources

Environment Management

• list_environments - List environments
• create_connection - Create environment connection
• get_connection - Get connection details
• get_environment - Get environment details
• get_environment_blueprint - Get environment blueprint
• get_environment_blueprint_configuration - Get environment blueprint configuration
• list_connections - List all connections
• list_environment_blueprints - List available blueprints
• list_environment_blueprint_configurations - List available blueprint configurations
• list_environment_profiles - List environment profiles

For detailed documentation of each tool with parameters and examples, see our Tool Reference.

Contributing

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Disclaimer

This is an unofficial, community-developed project and is not affiliated with, endorsed by, or supported by Amazon Web Services, Inc.

• AWS and DataZone are trademarks of Amazon.com, Inc. or its affiliates
• This project provides a community-built interface to Amazon DataZone APIs
• Users are responsible for their own AWS credentials, costs, and compliance
• No warranty or support is provided - use at your own risk
• Always follow AWS security best practices when using this tool

For official Amazon DataZone documentation and support, visit Amazon DataZone Documentation.

Support

• Documentation
• Issue Tracker
• Discussions

Acknowledgments

• Model Context Protocol for the protocol specification
• Amazon DataZone for the data governance platform
• The open-source community for inspiration and contributions