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DP-MCP Server

by devraj21
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DP-MCP Server

A comprehensive Data Platform MCP (Model Context Protocol) server that provides seamless integration between PostgreSQL databases and MinIO object storage. Built with FastMCP 2.0 for modern AI applications.

🚀 Features

PostgreSQL Operations

  • Query Execution: Execute SQL queries with results formatting
  • Schema Introspection: List tables, describe table structures
  • Data Export: Export table data to CSV format
  • Connection Management: Robust connection handling with SSL support

MinIO Object Storage

  • Bucket Management: Create, list, and manage buckets
  • Object Operations: Upload, download, list, and delete objects
  • Flexible Configuration: Support for secure and insecure connections

Advanced Capabilities

  • Database Backup: Automated PostgreSQL table backup to MinIO
  • Combined Operations: Seamless data pipeline between DB and storage
  • FastMCP Integration: Modern MCP server with HTTP transport
  • Docker Support: Complete development environment

🤖 AI-Powered Features (NEW!)

  • Natural Language Queries: Ask questions in plain English, get SQL results
  • Intelligent Query Analysis: AI-powered insights and pattern detection
  • Data Privacy Protection: Multiple security levels for sensitive data
  • Multi-Model Support: Claude, GPT, local Ollama models, or mock models
  • Secure by Design: Data never leaves your environment with local models

📋 Table of Contents

🏃 Quick Start

1. Setup the Project

# Clone and setup (if not already done) git clone <repository-url> cd da.dp-mcp chmod +x setup.sh ./setup.sh

The setup script will:

  • Install uv package manager if needed
  • Install all Python dependencies
  • Start Docker services automatically
  • Test your configuration

2. Configure Environment

# Copy and edit environment configuration cp .env.sample .env # Edit .env with your database and MinIO credentials if needed

Note: The default configuration works with the included Docker services.

3. Start Services

# Start PostgreSQL and MinIO with Docker docker-compose down docker-compose up -d # Verify services are healthy docker-compose ps

You should see both services as "healthy":

NAME STATUS dp-mcp-postgres Up (healthy) dp-mcp-minio Up (healthy)

4. Launch the MCP Server

# Start with debug logging (recommended for first run) uv run python src/dp_mcp/server.py --debug # Or for quiet mode uv run python src/dp_mcp/server.py

Successful startup looks like:

INFO:__main__:✓ PostgreSQL connection successful INFO:__main__:✓ MinIO connection successful INFO:__main__:Starting DP-MCP Server on port 8888 ╭─ FastMCP 2.0 ──────────────────────────────────────────────────────────────╮ │ 🖥️ Server name: dp-mcp │ │ 📦 Transport: Streamable-HTTP │ │ 🔗 Server URL: http://127.0.0.1:8888/mcp/ │ ╰────────────────────────────────────────────────────────────────────────────╯

5. Verify Installation

✅ Server Status: The server will be available at: http://127.0.0.1:8888/mcp/

✅ Test Connection:

# Quick health check (will show "Not Acceptable" - this is expected) curl -v http://localhost:8888/mcp/

✅ Access Development Services:

  • MinIO Console: http://localhost:9001 (minioadmin/minioadmin123)
  • PostgreSQL: localhost:5432 (dp_mcp_user/dp_mcp_password)

🎯 Alternative Running Methods

Run in Background:

# Start server in background uv run python src/dp_mcp/server.py > server.log 2>&1 & # Check if running ps aux | grep dp_mcp # Stop background server pkill -f "python src/dp_mcp/server.py"

Custom Host/Port:

# Run on different host/port uv run python src/dp_mcp/server.py --host 0.0.0.0 --port 9000 --debug

Using Entry Point (if installed):

# After running setup.sh, you can also use: dp-mcp --help dp-mcp --debug

🔧 Troubleshooting Quick Start

Services Won't Start:

# Check Docker status docker --version docker-compose --version # Restart services docker-compose restart # Check service logs docker-compose logs postgres docker-compose logs minio

Server Won't Start:

# Verify configuration uv run python src/dp_mcp/utils/config.py # Check dependencies uv sync # Ensure services are running first docker-compose ps

Connection Issues:

# Test database connection psql -h localhost -U dp_mcp_user -d dp_mcp_dev # Test MinIO connection curl http://localhost:9000/minio/health/live

🎉 You're Ready!

Your DP-MCP server is now running and ready to:

  • ✅ Execute PostgreSQL queries and manage database schemas
  • ✅ Upload, download, and manage MinIO objects and buckets
  • ✅ Perform automated database backups to object storage
  • ✅ Serve AI agents through the MCP protocol with 17+ specialized tools
  • ✅ NEW: Answer natural language questions about your data with AI
  • ✅ NEW: Generate intelligent insights and analysis reports

🤖 Test AI Features

To test the new AI capabilities:

# Test AI integration (works without API keys) python test_ai_integration.py # Start server with AI demo mode uv run python src/dp_mcp/server.py demo --debug # Test AI status via MCP client uv run python mcp_client.py --call get_ai_system_status

The server runs until you stop it with Ctrl+C. For production deployment, see the Deployment Guide.

📦 Installation

Prerequisites

  • Python 3.10 or higher
  • Docker and Docker Compose
  • uv package manager (installed automatically by setup.sh)

Manual Installation

# Install uv if not available curl -LsSf https://astral.sh/uv/install.sh | sh # Install dependencies uv sync # Optional: Install AI dependencies for cloud models uv sync --extra ai # Install in editable mode uv pip install -e .

Docker Services

The included docker-compose.yml provides:

  • PostgreSQL 15 with sample data
  • MinIO object storage with web console
  • Automatic health checks and initialization

⚙️ Configuration

Environment Variables

Create a .env file with the following configuration:

# PostgreSQL Configuration POSTGRES_HOST=localhost POSTGRES_PORT=5432 POSTGRES_USER=dp_mcp_user POSTGRES_PASSWORD=dp_mcp_password POSTGRES_DATABASE=dp_mcp_dev POSTGRES_SSL_MODE=prefer # MinIO Configuration MINIO_ENDPOINT=localhost:9000 MINIO_ACCESS_KEY=minioadmin MINIO_SECRET_KEY=minioadmin123 MINIO_SECURE=false MINIO_DEFAULT_BUCKET=default-bucket # MCP Server Configuration MCP_SERVER_HOST=127.0.0.1 MCP_SERVER_PORT=8888 DEBUG=false

Configuration Validation

# Test configuration uv run python src/dp_mcp/utils/config.py

🛠️ Available Tools

The server provides 17+ MCP tools for database, object storage, and AI-powered operations:

PostgreSQL Tools

ToolDescriptionParameters
execute_sql_queryExecute SQL queries with formattingquery (string), limit (int, default: 1000)
list_db_tablesList all tables in a schemaschema (string, default: "public")
describe_db_tableGet table structure detailstable_name (string), schema (string, default: "public")
export_table_csvExport table data as CSVtable_name (string), limit (int, default: 10000), where_clause (string, optional)

MinIO Object Storage Tools

ToolDescriptionParameters
list_minio_bucketsList all available bucketsNone
list_bucket_objectsList objects in a bucketbucket_name (string), prefix (string, optional), max_keys (int, default: 1000)
upload_to_minioUpload data to object storebucket_name (string), object_name (string), data (string), content_type (string, default: "text/plain")
download_from_minioDownload object from storagebucket_name (string), object_name (string)
create_minio_bucketCreate a new bucketbucket_name (string), region (string, optional)
delete_minio_objectDelete an objectbucket_name (string), object_name (string)

Combined Operations

ToolDescriptionParameters
backup_table_to_minioBackup PostgreSQL table to MinIOtable_name (string), bucket_name (string, default: "backups"), schema (string, default: "public"), limit (int, default: 10000), where_clause (string, optional)

🤖 AI-Enhanced Tools

ToolDescriptionParameters
ask_natural_language_queryConvert natural language to SQL and execute with AI analysisquestion (string), schema (string, default: "public"), model_name (string, optional)
explain_query_with_aiExecute SQL and get AI-powered explanationsql_query (string), limit (int, default: 100), model_name (string, optional)
get_ai_data_insightsGenerate AI suggestions for database analysisschema (string, default: "public"), model_name (string, optional)
analyze_table_patternsAI analysis of data patterns and qualitytable_name (string), schema (string, default: "public"), sample_size (int, default: 1000), model_name (string, optional)
generate_ai_data_reportComprehensive AI-powered data reportstitle (string), tables (string, comma-separated), schema (string, default: "public"), model_name (string, optional)
get_ai_system_statusGet AI system status and configurationNone

🤖 AI Features

Secure AI Integration

The DP-MCP server now includes comprehensive AI capabilities designed with security and privacy as top priorities:

🔒 Privacy Levels:

  • None: No filtering (public data only)
  • Basic: Remove obvious PII (emails, phones)
  • Moderate: Mask patterns, limit data size
  • Strict: Schema-only mode, heavy sanitization
  • Paranoid: No actual data sent to AI

🤖 Supported Models:

  • Claude (Anthropic): claude-3-sonnet, claude-3-haiku
  • OpenAI: gpt-4, gpt-3.5-turbo
  • Local Models: Ollama (llama2, codellama, mistral)
  • Mock Models: Safe testing without API calls

🛡️ Security Features:

  • Data sanitization and pattern masking
  • SQL injection detection
  • Audit logging for all AI interactions
  • Local-only processing with Ollama
  • Environment-based configurations

Quick AI Setup

🎯 Smart Auto-Configuration - No command-line parameters needed! The server automatically detects and uses whatever AI models you have available.

  1. Demo Mode (Works immediately):
# Just start the server - it works out of the box with mock AI uv run python src/dp_mcp/server.py --debug
  1. Local Models (Recommended for security):
# Install Ollama curl -fsSL https://ollama.com/install.sh | sh ollama serve # Install models (choose based on your needs): ollama pull phi3 # 2.2 GB - Fast, efficient ollama pull mistral # 4.1 GB - Balanced performance # Start server - automatically detects and uses Ollama models uv run python src/dp_mcp/server.py --debug
  1. Cloud Models (Optional - add API keys for premium models):
# Configure your API keys in .env.ai: # Replace CLAUDE_API_KEY=sk-ant-api03-XXXX... with your real key # Replace OPENAI_API_KEY=sk-XXXX... with your real key # Start server - automatically detects and uses all available models uv run python src/dp_mcp/server.py --debug

✨ Benefits of Auto-Configuration:

  • No complex parameters - Just start with uv run python src/dp_mcp/server.py
  • Smart detection - Uses Ollama models, cloud APIs, or demo mode automatically
  • User-friendly - Configure once in .env.ai, works everywhere
  • Flexible - Mix local and cloud models as needed

AI Usage Examples

# Natural language query result = await client.call_tool("ask_natural_language_query", { "question": "How many users signed up last month?" }) # AI query explanation result = await client.call_tool("explain_query_with_ai", { "sql_query": "SELECT COUNT(*) FROM users WHERE created_at >= '2024-01-01'" }) # Get AI insights result = await client.call_tool("get_ai_data_insights", { "schema": "public" }) # Analyze table patterns result = await client.call_tool("analyze_table_patterns", { "table_name": "users", "sample_size": 1000 }) # Generate comprehensive report result = await client.call_tool("generate_ai_data_report", { "title": "Monthly User Analysis", "tables": "users,user_activity,subscriptions" })

💡 Usage Examples

Basic Database Operations

# Connect to the MCP server and execute queries import mcp # List all tables result = await client.call_tool("list_db_tables", {"schema": "public"}) # Describe a specific table result = await client.call_tool("describe_db_table", { "table_name": "users", "schema": "public" }) # Execute a custom query result = await client.call_tool("execute_sql_query", { "query": "SELECT * FROM users WHERE is_active = true", "limit": 50 }) # Export table data result = await client.call_tool("export_table_csv", { "table_name": "orders", "where_clause": "order_date >= '2025-01-01'", "limit": 1000 })

Object Storage Operations

# List all buckets result = await client.call_tool("list_minio_buckets", {}) # Create a new bucket result = await client.call_tool("create_minio_bucket", { "bucket_name": "my-data-lake", "region": "us-east-1" }) # Upload data result = await client.call_tool("upload_to_minio", { "bucket_name": "my-data-lake", "object_name": "data/sample.json", "data": '{"key": "value"}', "content_type": "application/json" }) # List objects in a bucket result = await client.call_tool("list_bucket_objects", { "bucket_name": "my-data-lake", "prefix": "data/", "max_keys": 100 }) # Download an object result = await client.call_tool("download_from_minio", { "bucket_name": "my-data-lake", "object_name": "data/sample.json" })

Advanced: Database Backup to Object Storage

# Backup entire table to MinIO result = await client.call_tool("backup_table_to_minio", { "table_name": "users", "bucket_name": "backups", "schema": "public", "limit": 50000 }) # Backup with filtering result = await client.call_tool("backup_table_to_minio", { "table_name": "orders", "bucket_name": "data-lake", "where_clause": "created_at >= '2025-01-01'", "limit": 100000 })

🖥️ CLI Tool

The DP-MCP CLI tool provides direct command-line access to all PostgreSQL and MinIO operations without requiring the MCP protocol overhead.

Quick CLI Usage

# List all database tables ./dp-cli list-tables # Describe a table structure ./dp-cli describe-table users # Execute SQL queries ./dp-cli query "SELECT * FROM users LIMIT 5" # Export table to CSV ./dp-cli export-csv users --limit 100 --output users.csv # List MinIO buckets ./dp-cli list-buckets # Upload data to MinIO ./dp-cli upload default-bucket hello.txt "Hello, World!" # Download from MinIO ./dp-cli download default-bucket hello.txt --output downloaded.txt # Backup database table to MinIO ./dp-cli backup-table users --bucket backups --limit 1000

CLI Features

✅ 11 Commands: Direct access to all server tools
✅ Colored Output: Beautiful terminal interface
✅ File Operations: Upload/download from files
✅ Batch Processing: Perfect for scripting
✅ Error Handling: Comprehensive error messages

📖 Full CLI Documentation: CLI Reference Guide

🔌 MCP Client

The DP-MCP client enables MCP protocol communication for integration with AI agents.

Note: The MCP client is currently in development for full FastMCP compatibility. For immediate testing and usage, we recommend using the CLI Tool which provides direct access to all functionality.

Basic MCP Client Usage

# Test server connectivity uv run python mcp_client.py --ping # For full functionality, use the CLI tool instead: ./dp-cli list-tables ./dp-cli describe-table users

AI Agent Integration

For AI agents and applications that need MCP protocol access, the server is available at:

  • Server URL: http://127.0.0.1:8888/mcp/
  • Protocol: JSON-RPC 2.0 over HTTP with Server-Sent Events
  • Transport: FastMCP Streamable HTTP

MCP Client Status

🚧 In Development: Full MCP protocol client
✅ Session Management: Basic connectivity established
✅ Server Discovery: Endpoint detection working
⏳ Tool Calling: FastMCP protocol compatibility in progress

Recommended: Use the CLI Tool for immediate access to all 11 tools and full functionality.

📖 Full MCP Client Documentation: MCP Client Guide

📚 API Reference

Server Endpoints

The FastMCP server provides the following endpoints:

  • Base URL: http://127.0.0.1:8888/mcp/
  • Transport: Streamable HTTP (Server-Sent Events)
  • Protocol: MCP (Model Context Protocol) v1.0

Connection Information

# Server status and available tools GET http://127.0.0.1:8888/mcp/ # Tool execution (via MCP client) POST http://127.0.0.1:8888/mcp/

Error Handling

All tools implement comprehensive error handling:

  • Database Errors: Connection failures, SQL syntax errors, permission issues
  • Storage Errors: Bucket not found, object not found, access denied
  • Validation Errors: Invalid parameters, missing required fields
  • System Errors: Network issues, timeout errors

Response Format

All responses follow the MCP protocol format:

{ "jsonrpc": "2.0", "id": "request-id", "result": { "content": [ { "type": "text", "text": "Tool execution result" } ] } }

🔧 Development

Project Structure

da.dp-mcp/ ├── src/dp_mcp/ │ ├── __init__.py │ ├── server.py # Main FastMCP server │ ├── tools/ │ │ ├── __init__.py │ │ ├── postgres_tools.py # PostgreSQL operations │ │ └── minio_tools.py # MinIO operations │ └── utils/ │ ├── __init__.py │ └── config.py # Configuration management ├── tests/ │ ├── __init__.py │ └── test_server.py # Test suite ├── docker-compose.yml # Development services ├── init-db.sql # Sample database schema ├── setup.sh # Project setup script ├── pyproject.toml # Python project configuration ├── .env.sample # Environment template └── README.md # This file

Running Tests

# Install development dependencies uv sync --dev # Run tests uv run pytest # Run with coverage uv run pytest --cov=src/dp_mcp # Run specific test uv run pytest tests/test_server.py::test_postgres_connection

Code Quality

# Format code uv run black src/ tests/ # Sort imports uv run isort src/ tests/ # Type checking uv run mypy src/ # Linting uv run flake8 src/ tests/

Development Services

Access the development environment:

  • PostgreSQL: localhost:5432
    • Database: dp_mcp_dev
    • Username: dp_mcp_user
    • Password: dp_mcp_password
  • MinIO Console: http://localhost:9001
    • Username: minioadmin
    • Password: minioadmin123
  • MinIO API: http://localhost:9000

🚀 Deployment

Production Configuration

For production deployment, create a secure .env file:

# Production PostgreSQL POSTGRES_HOST=your-db-host.com POSTGRES_PORT=5432 POSTGRES_USER=production_user POSTGRES_PASSWORD=secure_password POSTGRES_DATABASE=production_db POSTGRES_SSL_MODE=require # Production MinIO MINIO_ENDPOINT=your-minio-host.com:9000 MINIO_ACCESS_KEY=production_access_key MINIO_SECRET_KEY=secure_secret_key MINIO_SECURE=true # Production Server MCP_SERVER_HOST=0.0.0.0 MCP_SERVER_PORT=8888 DEBUG=false

Docker Production Build

# Build production image docker build -t dp-mcp-server . # Run in production docker run -d \ --name dp-mcp-server \ --env-file .env \ -p 8888:8888 \ dp-mcp-server

Systemd Service

Create /etc/systemd/system/dp-mcp.service:

[Unit] Description=DP-MCP Server After=network.target [Service] Type=simple User=mcp WorkingDirectory=/opt/dp-mcp Environment=PATH=/opt/dp-mcp/.venv/bin ExecStart=/opt/dp-mcp/.venv/bin/python src/dp_mcp/server.py Restart=always RestartSec=10 [Install] WantedBy=multi-user.target

Health Checks

# Check server health curl -H "Accept: text/event-stream" http://localhost:8888/mcp/ # Monitor logs journalctl -u dp-mcp -f

🔍 Troubleshooting

Common Issues

Connection Refused Errors

# Check if services are running docker-compose ps # Restart services docker-compose restart # Check logs docker-compose logs postgres docker-compose logs minio

Authentication Errors

# Verify environment variables uv run python src/dp_mcp/utils/config.py # Test database connection psql -h localhost -U dp_mcp_user -d dp_mcp_dev # Test MinIO connection mc alias set local http://localhost:9000 minioadmin minioadmin123

Server Won't Start

# Check Python environment uv run python --version # Verify dependencies uv sync # Run with debug logging uv run python src/dp_mcp/server.py --debug

Debug Mode

Enable comprehensive logging:

# Start with full debug output uv run python src/dp_mcp/server.py --debug --host 0.0.0.0 --port 8888

Performance Tuning

For high-throughput scenarios:

# Increase connection pool size export POSTGRES_MAX_CONNECTIONS=20 # Adjust query limits export DEFAULT_QUERY_LIMIT=5000 # Enable connection pooling export POSTGRES_POOL_SIZE=10

🤝 Contributing

Development Setup

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/amazing-feature
  3. Make your changes and add tests
  4. Run the test suite: uv run pytest
  5. Format your code: uv run black src/ tests/
  6. Submit a pull request

Code Standards

  • Follow PEP 8 style guidelines
  • Add type hints for all functions
  • Write comprehensive docstrings
  • Include unit tests for new features
  • Update documentation for API changes

Reporting Issues

Please include:

  • Python version and operating system
  • Complete error messages and stack traces
  • Steps to reproduce the issue
  • Configuration details (without sensitive data)

📄 License

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

🙏 Acknowledgments

For more information, visit the project documentation or contact the development team.

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

A comprehensive Data Platform server that enables seamless integration between PostgreSQL databases and MinIO object storage, with AI-powered natural language query capabilities.

  1. 🚀 Features
    1. PostgreSQL Operations
    2. MinIO Object Storage
    3. Advanced Capabilities
    4. 🤖 AI-Powered Features (NEW!)
  2. 📋 Table of Contents
    1. 🏃 Quick Start
      1. 1. Setup the Project
      2. 2. Configure Environment
      3. 3. Start Services
      4. 4. Launch the MCP Server
      5. 5. Verify Installation
      6. 🎯 Alternative Running Methods
      7. 🔧 Troubleshooting Quick Start
      8. 🎉 You're Ready!
      9. 🤖 Test AI Features
    2. 📦 Installation
      1. Prerequisites
      2. Manual Installation
      3. Docker Services
    3. ⚙️ Configuration
      1. Environment Variables
      2. Configuration Validation
    4. 🛠️ Available Tools
      1. PostgreSQL Tools
      2. MinIO Object Storage Tools
      3. Combined Operations
      4. 🤖 AI-Enhanced Tools
    5. 🤖 AI Features
      1. Secure AI Integration
      2. Quick AI Setup
      3. AI Usage Examples
    6. 💡 Usage Examples
      1. Basic Database Operations
      2. Object Storage Operations
      3. Advanced: Database Backup to Object Storage
    7. 🖥️ CLI Tool
      1. Quick CLI Usage
      2. CLI Features
    8. 🔌 MCP Client
      1. Basic MCP Client Usage
      2. AI Agent Integration
      3. MCP Client Status
    9. 📚 API Reference
      1. Server Endpoints
      2. Connection Information
      3. Error Handling
      4. Response Format
    10. 🔧 Development
      1. Project Structure
      2. Running Tests
      3. Code Quality
      4. Development Services
    11. 🚀 Deployment
      1. Production Configuration
      2. Docker Production Build
      3. Systemd Service
      4. Health Checks
    12. 🔍 Troubleshooting
      1. Common Issues
      2. Debug Mode
      3. Performance Tuning
    13. 🤝 Contributing
      1. Development Setup
      2. Code Standards
      3. Reporting Issues
    14. 📄 License
      1. 🙏 Acknowledgments

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