Gergy AI - MCP Architecture Foundation

Gergy AI is an intelligent assistant powered by Model Context Protocol (MCP) architecture, designed to provide cross-domain intelligence across five key life areas: Financial, Family, Lifestyle, Professional, and Home management.

🏗️ Architecture Overview

This foundational implementation provides the shared infrastructure that all five MCP servers will build upon:

Shared Infrastructure

• Database Layer: PostgreSQL with JSONB for flexible schema and cross-domain knowledge storage
• Caching Layer: Redis for high-performance caching with cross-domain relevance
• Pattern Recognition: Intelligent cross-domain pattern detection and suggestions
• Cost Management: Distributed API budget tracking and optimization
• Base MCP Framework: Foundation class for all domain servers

Domain Servers

1. Financial Server - Budget management, expense tracking, investment insights
2. Family Server - Event planning, relationship management, family coordination
3. Lifestyle Server - Health, fitness, personal development, leisure activities
4. Professional Server - Career development, skill tracking, professional networking
5. Home Server - Home maintenance, improvement projects, household management

🚀 Quick Start

Prerequisites

• Python 3.10+ (tested with 3.10.12)
• Docker and Docker Compose
• PostgreSQL client tools (optional for manual access)
• Redis client tools (optional for manual access)

Note: PostgreSQL and Redis will run in Docker containers, so you don't need them installed locally.

Installation

1. Clone and setup:

git clone <repository-url> gergy-mcp
cd gergy-mcp

2. Environment configuration:

cp .env.example .env
# Edit .env with your specific configuration

3. Start the infrastructure:

docker-compose up -d postgres redis

4. Install dependencies:

pip install -r requirements.txt

5. Initialize database:

python -c "
from shared.models.database import DatabaseConfig
config = DatabaseConfig('postgresql://gergy_user:gergy_password@localhost:5432/gergy_knowledge')
config.create_tables()
print('Database initialized successfully')
"

📁 Project Structure

gergy-mcp/
├── shared/                          # Shared infrastructure
│   ├── models/                      # Database models
│   │   ├── __init__.py
│   │   └── database.py             # PostgreSQL models with JSONB
│   ├── services/                    # Core services
│   │   ├── __init__.py
│   │   ├── database_service.py     # Unified knowledge access
│   │   ├── pattern_recognition_service.py  # Cross-domain intelligence
│   │   ├── cost_tracking_service.py        # API budget management
│   │   └── cache_service.py        # Redis caching with relevance
│   ├── utils/                       # Utilities
│   │   ├── __init__.py
│   │   └── config.py               # Configuration management
│   ├── __init__.py
│   └── base_mcp_server.py          # Base server framework
├── servers/                         # Domain-specific servers
│   ├── financial/                   # Financial management server
│   ├── family/                      # Family coordination server
│   ├── lifestyle/                   # Lifestyle management server
│   ├── professional/                # Professional development server
│   └── home/                        # Home management server
├── docker-compose.yml               # Infrastructure orchestration
├── requirements.txt                 # Python dependencies
├── .env.example                     # Environment configuration template
└── README.md                        # This file

🛠️ Key Features

Cross-Domain Intelligence

• Pattern Recognition: Automatically detects patterns across domains (e.g., financial decisions affecting family plans)
• Knowledge Sharing: Unified knowledge base accessible across all servers
• Context Awareness: Maintains conversation context and suggests relevant cross-domain insights

Performance & Cost Optimization

• Smart Caching: Redis-based caching with cross-domain relevance scoring
• Cost Tracking: Real-time API usage monitoring with budget alerts
• Pattern-Based Suggestions: Reduces API calls through intelligent pattern matching

Scalable Architecture

• Modular Design: Each domain server inherits from BaseMCPServer
• Database Flexibility: JSONB fields allow schema evolution without migrations
• Containerized Deployment: Docker Compose for easy scaling and deployment

📊 Database Schema

Core Tables

• knowledge_items: Cross-domain knowledge with flexible JSONB metadata
• user_sessions: Conversation tracking and context accumulation
• temporal_cache: Expiration management and cross-module relevance
• cross_domain_patterns: Pattern recognition system
• api_usage_analytics: Cost tracking per server

Example Usage

from shared.services.database_service import DatabaseService
from shared.services.pattern_recognition_service import PatternRecognitionService

# Initialize services
db_service = DatabaseService("postgresql://...")
pattern_service = PatternRecognitionService(db_service)

# Store knowledge across domains
await db_service.store_knowledge(
    domain="financial",
    title="Budget Planning",
    content="Monthly budget analysis...",
    metadata={"category": "planning", "priority": "high"},
    keywords=["budget", "planning", "monthly"]
)

# Detect cross-domain patterns
patterns = await pattern_service.analyze_conversation(
    content="Planning a family vacation",
    domain="family",
    session_id="user_123"
)

🔧 Configuration

Environment Variables

Key configuration options in .env:

# Database
DATABASE_URL=postgresql://gergy_user:gergy_password@localhost:5432/gergy_knowledge

# Redis
REDIS_URL=redis://localhost:6379

# Budget limits per server (USD/day)
FINANCIAL_BUDGET_LIMIT=15.0
FAMILY_BUDGET_LIMIT=10.0
LIFESTYLE_BUDGET_LIMIT=8.0
PROFESSIONAL_BUDGET_LIMIT=12.0
HOME_BUDGET_LIMIT=8.0

Server Configuration

Each domain server can be configured independently:

from shared.utils.config import load_config

config = load_config("config.yml")  # Optional config file
financial_config = config.servers["financial"]

🔍 Monitoring & Analytics

Built-in Metrics

• Request/response tracking per server
• Cost analysis and budget alerts
• Pattern detection effectiveness
• Cache hit/miss ratios
• Cross-domain suggestion accuracy

Optional Monitoring Stack

• Grafana: Dashboards for visual monitoring
• Prometheus: Metrics collection and alerting
• Database Analytics: Cross-domain usage patterns

🧪 Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=shared

# Run specific test modules
pytest tests/test_database_service.py
pytest tests/test_pattern_recognition.py

📈 Next Steps

This foundation enables:

1. Domain Server Implementation: Each server will inherit from BaseMCPServer
2. Tool Registration: Domain-specific tools for Claude.ai integration
3. Pattern Learning: Machine learning models for better pattern recognition
4. API Integration: External service connections with cost tracking
5. Advanced Analytics: Cross-domain insights and optimization

🤝 Contributing

1. Follow the established patterns in BaseMCPServer
2. Ensure all new features include tests
3. Update documentation for new configurations
4. Maintain cross-domain compatibility

📝 License

[Your chosen license]

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Status: Foundation Complete ✅ Next Phase: Domain Server Implementation Target: Full MCP integration with Claude.ai