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

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

Related MCP server: Home Assistant MCP Server

🚀 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

Clone and setup:

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

Environment configuration:

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

Start the infrastructure:

docker-compose up -d postgres redis

Install dependencies:

pip install -r requirements.txt

Initialize database:

python -c " from shared.models.database import DatabaseConfig import os config = DatabaseConfig(os.getenv('DATABASE_URL')) 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://username: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:

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

🤝 Contributing

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

📝 License

[Your chosen license]

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

Gergy AI MCP