S3 Data Lake Server

License Python AWS MCP

Transform your S3 data lakes into AI-accessible knowledge bases with natural language queries

A production-ready Model Context Protocol (MCP) server that gives AI agents seamless access to S3 data lakes. Built by a senior developer with 15+ years of experience in AI/ML, agents, and AWS Bedrock systems.

🎯 Why This Exists

I was building ETL systems for AI agents and kept hitting the same wall: How do you give agents seamless access to data lakes without building custom APIs for every single use case?

Then AWS Bedrock AgentCore dropped MCP support, and everything clicked. This MCP server bridges that gap, turning your S3 data lakes into agent-accessible knowledge bases with natural language queries.

✨ Key Features

🔥 8 Powerful Tools - Complete S3 data lake operations
📊 Multi-Format Support - CSV, JSON, Parquet with intelligent processing
⚡ FastMCP Framework - Modern, high-performance MCP server
🏗️ AgentCore Runtime - Serverless, auto-scaling deployment
🛡️ Production-Grade - Comprehensive error handling, monitoring, security
🎯 Type-Safe - Full Python type hints and validation
🚀 Deploy in Minutes - UV package management, one-command deployment

🛠️ Available Tools

Tool	Description	Use Case
list_s3_buckets	List accessible S3 buckets	Data discovery
list_s3_objects	Browse bucket contents with filtering	Dataset exploration
read_csv_from_s3	Parse CSV files with metadata	Tabular data analysis
read_json_from_s3	Process JSON objects and arrays	Complex data structures
read_parquet_from_s3	Columnar data with full type info	High-performance analytics
query_csv_data	Filter and query with smart typing	Data querying
get_dataset_summary	Statistical analysis and profiling	Data understanding
get_file_metadata	Comprehensive file information	Metadata exploration

🚀 Quick Start

Prerequisites

• Python 3.12+
• UV package manager
• AWS CLI configured
• AWS Bedrock AgentCore access

1. Install & Setup

# Install UV (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone and install dependencies
git clone https://github.com/anespo/s3-data-lake-mcp-server.git
cd s3-data-lake-mcp-server
uv sync

2. Local Development

# Run the MCP server locally
uv run python run_local.py

# Test in another terminal
uv run pytest tests/ -v

3. Deploy to AWS AgentCore Runtime

# One-command deployment
uv run python deploy_uv.py

# Your Agent ARN will be displayed for integration

4. Generate Demo Data (Optional)

# Create 66.7MB of demo datasets
uv run python generate_mock_data.py

💬 Natural Language Queries

Once integrated with your AI agents, you can ask questions like:

Data Discovery:

• "What S3 buckets do I have access to?"
• "Show me all datasets in my analytics bucket"
• "List CSV files larger than 10MB"

Data Analysis:

• "Read the customer analytics data and show me the first 10 rows"
• "Find all sales transactions over $50,000"
• "What columns are available in the IoT sensor data?"
• "Show me customers in the Technology industry"

Metadata & Insights:

• "What's the total size of data in my bucket?"
• "How many records are in each dataset?"
• "Give me a statistical summary of the sales data"

🏗️ Architecture

Built on Modern Stack:

• 🏗️ AWS Bedrock AgentCore Runtime - Serverless, auto-scaling
• ⚡ FastMCP Framework - High-performance MCP server
• 📦 UV Package Manager - Ultra-fast Python dependency management
• 🔧 boto3 + pandas + pyarrow - Efficient data processing
• 🛡️ AWS SigV4 + IAM - Enterprise-grade security

🔗 Integration Examples

Kiro IDE

{
  "mcpServers": {
    "s3-data-lake": {
      "command": "python",
      "args": ["kiro_s3_mcp_wrapper.py"],
      "env": {
        "AWS_REGION": "eu-west-1",
        "AWS_PROFILE": "default"
      }
    }
  }
}

Strands Agents

from strands import Agent
from strands.tools.mcp import MCPClient

# Connect to deployed AgentCore Runtime
agent_arn = "arn:aws:bedrock-agentcore:eu-west-1:123456789012:runtime/s3-data-lake-mcp-server"
mcp_client = MCPClient(agent_arn)

agent = Agent(
    name="Data Lake Analyst",
    description="AI agent with S3 data lake access",
    tools=mcp_client.list_tools_sync()
)

# Natural language data analysis
response = agent("Analyze customer data and find high-value segments")

📊 Demo Environment

The repository includes a complete demo environment with:

• 66.7MB of realistic mock data across 3 formats
• Customer Analytics (CSV, 50K records) - Business intelligence data
• Sales Transactions (JSON, 75K records) - Financial analysis data
• IoT Sensor Data (Parquet, 100K records) - Time-series analytics data

Perfect for presentations, testing, and showcasing capabilities without exposing real data.

🧪 Testing & Quality

# Run comprehensive test suite
uv run pytest tests/ -v --cov=src

# Test specific functionality
uv run pytest tests/test_s3_mcp_server.py::test_read_csv_from_s3 -v

# Test deployed MCP server
uv run python test_deployed_mcp.py

Quality Assurance:

• ✅ 95%+ test coverage
• ✅ Type safety with mypy
• ✅ Production error handling
• ✅ Performance benchmarking
• ✅ Security validation

📚 Documentation

Document	Description
🚀 Deployment Guide	Complete deployment instructions
🏗️ Architecture	System design and components
🔗 Integration Guide	Kiro and Strands integration
📋 API Reference	Full tool documentation

🛡️ Security & Compliance

• 🔐 AWS SigV4 Authentication - Industry-standard request signing
• 🎯 IAM Role-Based Access - Least privilege principle
• 🔒 No Hardcoded Credentials - Secure credential management
• 📊 Comprehensive Logging - Full audit trail
• 🛡️ Error Sanitization - No sensitive data in logs

📈 Monitoring & Observability

• 📊 CloudWatch Integration - Centralized logging and metrics
• 🎯 GenAI Observability - Specialized AI/ML monitoring
• ⚡ Performance Tracking - Request latency and throughput
• 🚨 Error Alerting - Proactive issue detection

🚀 What's Next?

Planned Enhancements:

• 🌍 Multi-region deployment support
• 🔍 Advanced query capabilities (SQL-like syntax)
• 📡 Real-time streaming data support
• 🚀 Enhanced caching layer (Redis/ElastiCache)
• 🤖 ML model integration for data insights
• 🔌 Plugin architecture for custom tools

🤝 Contributing

Built by the community, for the community:

1. 🍴 Fork the repository
2. 🌟 Create a feature branch
3. ✨ Add your improvements
4. 🧪 Add comprehensive tests
5. 📝 Update documentation
6. 🚀 Submit a pull request

📄 License

This project is licensed under a custom license allowing non-commercial use. See LICENSE for details.

👨‍💻 About the Author

Built by Tony Esposito

Turning complex data infrastructure into simple, agent-accessible APIs.

🙋‍♂️ Support & Community

• 📖 Documentation: Comprehensive guides in /docs
• 🐛 Issues: GitHub Issues for bugs and feature requests
• 💬 Discussions: GitHub Discussions for questions
• 📧 Contact: [email protected]

⭐ Star This Repository

If this MCP server helps your AI agents access S3 data lakes, please star the repository! It helps others discover this tool and motivates continued development.

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🚀 Ready to give your AI agents superpowers with S3 data lake access? Deploy in minutes and start querying with natural language today!