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MCP Dataset Onboarding Server

by Magenta91
GitHub
Python
MIT License
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🤖 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

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

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

📄 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! 🚀

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Enables automated dataset processing and onboarding using Google Drive integration. Provides metadata extraction, data quality assessment, and contract generation for CSV/Excel files through natural language interactions.

  1. 🔒 SECURITY FIRST - READ THIS BEFORE SETUP
    1. Features
      1. Project Structure
        1. 🚀 Quick Start
          1. 1. Security Setup (REQUIRED)
          2. 2. Installation
          3. 3. Choose Your Interface
        2. 🎯 Usage Scenarios
          1. Scenario 1: Set-and-Forget Automation
          2. Scenario 2: LLM-Powered Data Analysis
          3. Scenario 3: API Integration
        3. 📊 What You Get
          1. 🛠️ Available Tools
            1. FastAPI Endpoints
            2. MCP Tools (for LLMs)
            3. CLI Commands
          2. 🔧 Configuration
            1. Environment Variables (.env)
            2. Auto-Processor Settings (auto_config.py)
          3. 📈 Monitoring & Analytics
            1. 🐳 Docker Deployment
              1. 🔍 Troubleshooting
                1. Common Issues
                2. Debug Commands
              2. 🤝 Contributing
                1. 📚 Documentation
                  1. 📄 License
                    1. 🎉 What Makes This Special

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