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Gergy AI MCP

by k4nuck
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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
  1. Environment configuration:
cp .env.example .env # Edit .env with your specific configuration
  1. Start the infrastructure:
docker-compose up -d postgres redis
  1. Install dependencies:
pip install -r requirements.txt
  1. 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:

  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]

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

This server cannot be installed

-
security - not tested
F
license - not found
-
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.

An intelligent assistant built on Model Context Protocol architecture that provides cross-domain intelligence across financial, family, lifestyle, professional, and home management domains.

  1. 🏗️ Architecture Overview
    1. Shared Infrastructure
    2. Domain Servers
  2. 🚀 Quick Start
    1. Prerequisites
    2. Installation
  3. 📁 Project Structure
    1. 🛠️ Key Features
      1. Cross-Domain Intelligence
      2. Performance & Cost Optimization
      3. Scalable Architecture
    2. 📊 Database Schema
      1. Core Tables
      2. Example Usage
    3. 🔧 Configuration
      1. Environment Variables
      2. Server Configuration
    4. 🔍 Monitoring & Analytics
      1. Built-in Metrics
      2. Optional Monitoring Stack
    5. 🧪 Testing
      1. 📈 Next Steps
        1. 🤝 Contributing
          1. 📝 License

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