🤖 MCP Dataset Onboarding Server

A FastAPI-based MCP (Model-Compatible Protocol) server for automating dataset onboarding using Google Drive as both input source and mock catalog.

🔒 SECURITY FIRST - READ THIS BEFORE SETUP

⚠️ This repository contains template files only. You MUST configure your own credentials before use.

📖 Read SECURITY_SETUP.md for complete security instructions.

🚨 Never commit service account keys or real folder IDs to version control!

Related MCP server: Google Drive MCP Server

Features

• Automated Dataset Processing: Complete workflow from raw CSV/Excel files to cataloged datasets

• Google Drive Integration: Uses Google Drive folders as input source and catalog storage

• Metadata Extraction: Automatically extracts column information, data types, and basic statistics

• Data Quality Rules: Suggests DQ rules based on data characteristics

• Contract Generation: Creates Excel contracts with schema and DQ information

• Mock Catalog: Publishes processed artifacts to a catalog folder

• 🤖 Automated Processing: Watches folders and processes files automatically

• 🌐 Multiple Interfaces: FastAPI server, MCP server, CLI tools, and dashboards

Project Structure

├── main.py                    # FastAPI server and endpoints
├── mcp_server.py             # True MCP protocol server for LLM integration
├── utils.py                   # Google Drive helpers and DQ functions
├── dataset_processor.py       # Centralized dataset processing logic
├── auto_processor.py         # 🤖 Automated file monitoring
├── start_auto_processor.py   # 🚀 Easy startup for auto-processor
├── processor_dashboard.py    # 📊 Monitoring dashboard
├── dataset_manager.py        # CLI tool for managing datasets
├── local_test.py             # Local processing script
├── auto_config.py           # ⚙️ Configuration management
├── requirements.txt          # Python dependencies
├── Dockerfile               # Container configuration
├── .env.template            # Environment variables template
├── .gitignore               # Security: excludes sensitive files
├── SECURITY_SETUP.md        # 🔒 Security configuration guide
├── processed_datasets/      # Organized output folder
│   └── [dataset_name]/      # Individual dataset folders
│       ├── [dataset].csv    # Original dataset
│       ├── [dataset]_metadata.json
│       ├── [dataset]_contract.xlsx
│       ├── [dataset]_dq_report.json
│       └── README.md        # Dataset summary
└── README.md               # This file

🚀 Quick Start

1. Security Setup (REQUIRED)

# 1. Read the security guide
cat SECURITY_SETUP.md

# 2. Set up your Google service account (outside this repo)
# 3. Configure your environment variables
cp .env.template .env
# Edit .env with your actual values

# 4. Verify no sensitive files will be committed
git status

2. Installation

# Install dependencies
pip install -r requirements.txt

# Test the setup
python local_test.py

3. Choose Your Interface

🤖 Fully Automated (Recommended)

# Start auto-processor - upload files and walk away!
python start_auto_processor.py

🌐 API Server

# Start FastAPI server
python main.py

🧠 LLM Integration (MCP)

# Start MCP server for Claude Desktop, etc.
python mcp_server.py

🖥️ Command Line

# Manual dataset management
python dataset_manager.py list
python dataset_manager.py process YOUR_FILE_ID

🎯 Usage Scenarios

Scenario 1: Set-and-Forget Automation

1. python start_auto_processor.py

2. Upload files to Google Drive

3. Files processed automatically within 30 seconds

4. Monitor with python processor_dashboard.py --live

Scenario 2: LLM-Powered Data Analysis

1. Configure MCP server in Claude Desktop

2. Chat: "Analyze the dataset I just uploaded"

3. Claude uses MCP tools to process and explain your data

Scenario 3: API Integration

1. python main.py

2. Integrate with your data pipelines via REST API

3. Programmatic dataset onboarding

📊 What You Get

For each processed dataset:

• 📄 Original File: Preserved in organized folder

• 📋 Metadata JSON: Column info, types, statistics

• 📊 Excel Contract: Professional multi-sheet contract

• 🔍 Quality Report: Data quality assessment

• 📖 README: Human-readable summary

🛠️ Available Tools

FastAPI Endpoints

• /tool/extract_metadata - Analyze dataset structure

• /tool/apply_dq_rules - Generate quality rules

• /process_dataset - Complete workflow

• /health - System health check

MCP Tools (for LLMs)

• extract_dataset_metadata - Dataset analysis

• generate_data_quality_rules - Quality assessment

• process_complete_dataset - Full pipeline

• list_catalog_files - Catalog browsing

CLI Commands

• dataset_manager.py list - Show processed datasets

• auto_processor.py --once - Single check cycle

• processor_dashboard.py --live - Real-time monitoring

🔧 Configuration

Environment Variables (.env)

GOOGLE_SERVICE_ACCOUNT_KEY_PATH=path/to/your/key.json
MCP_SERVER_FOLDER_ID=your_input_folder_id
MCP_CLIENT_FOLDER_ID=your_output_folder_id

Auto-Processor Settings (auto_config.py)

• Check interval: 30 seconds

• Supported formats: CSV, Excel

• File age threshold: 1 minute

• Max files per cycle: 5

📈 Monitoring & Analytics

# Current status
python processor_dashboard.py

# Live monitoring (auto-refresh)
python processor_dashboard.py --live

# Detailed statistics
python processor_dashboard.py --stats

# Processing history
python auto_processor.py --list

🐳 Docker Deployment

# Build
docker build -t mcp-dataset-server .

# Run (mount your service account key securely)
docker run -p 8000:8000 \
  -v /secure/path/to/key.json:/app/keys/key.json \
  -e GOOGLE_SERVICE_ACCOUNT_KEY_PATH=/app/keys/key.json \
  -e MCP_SERVER_FOLDER_ID=your_folder_id \
  mcp-dataset-server

🔍 Troubleshooting

Common Issues

• No files detected: Check Google Drive permissions

• Processing errors: Verify service account access

• MCP not working: Check Claude Desktop configuration

Debug Commands

# Test Google Drive connection
python -c "from utils import get_drive_service; print('✅ Connected')"

# Check auto-processor status
python auto_processor.py --once

# Verify MCP server
python test_mcp_server.py

🤝 Contributing

1. Fork the repository

2. Create a feature branch

3. Never commit sensitive data

4. Test your changes

5. Submit a pull request

📚 Documentation

• SECURITY_SETUP.md - Security configuration

• AUTOMATION_GUIDE.md - Automation features

• MCP_INTEGRATION_GUIDE.md - LLM integration

📄 License

MIT License

🎉 What Makes This Special

• 🔒 Security First: Proper credential management

• 🤖 True Automation: Zero manual intervention

• 🧠 LLM Integration: Natural language data processing

• 📊 Professional Output: Enterprise-ready documentation

• 🔧 Multiple Interfaces: API, CLI, MCP, Dashboard

• 📈 Real-time Monitoring: Live processing status

• 🗂️ Perfect Organization: Structured output folders

Transform your messy data files into professional, documented, quality-checked datasets automatically! 🚀