Which integrations are available for this server?

Used for containerized deployment and infrastructure orchestration of the MCP server components Environment configuration management for storing server settings and API credentials Optional integration for visual monitoring through dashboards Provides the database layer with JSONB for flexible schema and cross-domain knowledge storage Optional integration for metrics collection and alerting Testing framework integration for running tests and coverage analysis Core programming language requirement (3.10+) for the MCP server implementation Implements high-performance caching with cross-domain relevance scoring

How do I use Gergy AI MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Gergy AI MCP analyze my spending patterns and suggest family budget adjustments" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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