isa-mcp-cli 1.0.0

ISA MCP CLI - Comprehensive command-line interface for MCP server with RAG capabilities

isA_MCP - AI-Powered Smart MCP Server

🎯 Project Overview

isA_MCP is a sophisticated AI-powered Smart MCP (Model Context Protocol) Server that has evolved into an intelligent, enterprise-grade platform with comprehensive service integrations and automated capability discovery.

🤖 Key Features

• 🧠 AI-Powered Tool Selection - Intelligent tool recommendation based on natural language queries
• 📊 Data Analytics Suite - Complete 5-step data processing workflow with LLM-powered SQL generation
• 🕸️ Advanced Web Services - Modern web scraping with anti-detection and JavaScript execution
• 🛍️ E-commerce Integration - Full Shopify integration with cart and checkout management
• 📚 RAG & Document Analytics - Retrieval-augmented generation with multi-format document processing
• 🖼️ AI Image Generation - Image creation and transformation capabilities
• 🧮 Memory Management - Persistent information storage with intelligent retrieval
• 🔐 Enterprise Security - Multi-level authorization with audit logging
• 🐳 Production-Ready - Docker cluster deployment with load balancing

🏗️ System Architecture

graph TB
    subgraph "Load Balancer"
        LB[Nginx Load Balancer :8081]
    end
    
    subgraph "Smart MCP Cluster"
        S1[Smart MCP Server :4321]
        S2[Smart MCP Server :4322]
        S3[Smart MCP Server :4323]
    end
    
    subgraph "AI Core"
        AD[Auto Discovery]
        TS[Tool Selector]
        PS[Prompt Selector]
    end
    
    subgraph "Services Layer"
        DA[Data Analytics]
        WS[Web Services]
        RAG[RAG Service]
        SH[Shopify]
        IM[Image Gen]
        MEM[Memory]
        EV[Event Sourcing]
    end
    
    subgraph "Data Layer"
        PG[(PostgreSQL/Supabase)]
        MY[(MySQL)]
        SS[(SQL Server)]
        VEC[(Vector Store)]
    end
    
    LB --> S1
    LB --> S2
    LB --> S3
    
    S1 --> AD
    S2 --> AD
    S3 --> AD
    
    AD --> TS
    AD --> PS
    
    TS --> DA
    TS --> WS
    TS --> RAG
    TS --> SH
    TS --> IM
    TS --> MEM
    TS --> EV
    
    DA --> PG
    DA --> MY
    DA --> SS
    RAG --> VEC
    WS --> PG

🚀 Quick Start

Requirements

• Python 3.11+
• Docker & Docker Compose
• PostgreSQL 14+ (with pgvector extension)
• Redis 6+ (for caching and sessions)

Installation

1. Clone the repository:

git clone <repository_url>
cd isA_MCP

2. Install dependencies:

pip install -r requirements.txt

3. Environment setup:

# Copy and configure environment variables
cp .env.example .env
# Edit .env with your database credentials and API keys

4. Database setup:

# Start PostgreSQL with pgvector extension
docker-compose up -d postgres

5. Start the Smart MCP Server:

Option A: Single Server (Development)

python smart_mcp_server.py

Option B: Production Cluster

# Start complete cluster with load balancer
docker-compose up -d
# Access via http://localhost:8081

Option C: Railway Deployment

# One-click deployment to Railway
railway up

🛠️ Services & Capabilities

📊 Data Analytics Service

Complete 5-Step Data Processing Workflow

• Step 1: Metadata extraction from databases (PostgreSQL, MySQL, SQL Server) and files
• Step 2: Semantic enrichment with business entity identification
• Step 3: Embedding generation and vector storage (pgvector)
• Step 4: Natural language query matching using semantic similarity
• Step 5: LLM-powered SQL generation with fallback strategies
• Tools: data_sourcing, data_query

🕸️ Web Services Platform

Modern Web Scraping with AI Enhancement

• Multi-provider search integration (Brave API)
• Playwright browser automation with stealth capabilities
• LLM-powered content extraction with predefined schemas
• AI-enhanced filtering and relevance scoring
• Human behavior simulation for anti-detection
• Tools: scrape_webpage, scrape_multiple_pages, extract_page_links, search_page_content

📚 RAG & Document Analytics

Intelligent Document Processing

• Supabase pgvector integration for vector storage
• Multi-format document processing (PDF, DOC, DOCX, PPT, PPTX, TXT)
• Quick RAG question-answering for documents
• Multi-collection support with user isolation
• Tools: search_rag_documents, add_rag_documents, quick_rag_question

🛍️ Shopify E-commerce Integration

Complete E-commerce Workflow

• Product search and catalog management
• Shopping cart operations and management
• Checkout and payment processing (test environment)
• Customer profile and shipping address management
• Tools: search_products, add_to_cart, view_cart, start_checkout, process_payment

🖼️ AI Image Generation

Creative AI Services

• AI image creation with custom prompts
• Image-to-image transformation capabilities
• File-based image generation and storage
• Tools: generate_image, generate_image_to_file, image_to_image

🧮 Memory Management System

Persistent Information Storage

• Categorized memory storage with keyword tagging
• Intelligent memory retrieval and search
• Secure memory deletion with authorization
• Tools: remember, forget, update_memory, search_memories

🔄 Event Sourcing & Background Tasks

Asynchronous Processing

• Background task creation and lifecycle management
• Event-driven architecture support
• Task monitoring and control (pause/resume/delete)
• Tools: create_background_task, list_background_tasks, pause_background_task

🔐 Security & Administration

Enterprise-Grade Security

• Multi-level authorization (LOW, MEDIUM, HIGH)
• JWT-based authentication with bcrypt password hashing
• Comprehensive audit logging and monitoring
• Human-in-the-loop interaction workflows
• Tools: request_authorization, check_security_status, get_audit_log

🤖 AI-Powered Features

Auto-Discovery System

The Smart MCP Server automatically discovers and registers all available tools, prompts, and resources:

# Auto-discovery extracts metadata from docstrings and function signatures
from core.auto_discovery import AutoDiscovery

discovery = AutoDiscovery()
tools = await discovery.discover_tools()  # Finds all MCP tools
prompts = await discovery.discover_prompts()  # Extracts prompts
resources = await discovery.discover_resources()  # Identifies resources

AI Tool Selection

Intelligent tool recommendation based on natural language queries:

from core.ai_selectors import ToolSelector

selector = ToolSelector()
# Natural language query gets matched to appropriate tools
tools = await selector.select_tools("I need to analyze sales data from my database")
# Returns: ['data_sourcing', 'data_query'] with confidence scores

📚 API Documentation

Data Analytics API

# Extract database metadata and create embeddings
await client.call_tool("data_sourcing", {
    "connection_string": "postgresql://user:pass@host:5432/db",
    "tables": ["sales", "customers"]
})

# Query data using natural language
await client.call_tool("data_query", {
    "query": "Show me top 10 customers by revenue this month",
    "connection_string": "postgresql://user:pass@host:5432/db"
})

Web Services API

# Advanced web scraping with AI extraction
await client.call_tool("scrape_webpage", {
    "url": "https://example.com",
    "extraction_schema": {
        "products": ["name", "price", "description"]
    },
    "use_stealth": True
})

# Multi-page scraping with pagination
await client.call_tool("scrape_multiple_pages", {
    "base_url": "https://example.com/products",
    "max_pages": 10,
    "extraction_schema": {"products": ["name", "price"]}
})

RAG & Document Analytics API

# Quick document Q&A
await client.call_tool("quick_rag_question", {
    "file_path": "/path/to/document.pdf",
    "question": "What are the key findings in this report?"
})

# Add documents to RAG collection
await client.call_tool("add_rag_documents", {
    "collection_name": "company_docs",
    "documents": ["Document content..."],
    "metadatas": [{"source": "report.pdf"}]
})

# Search RAG documents
await client.call_tool("search_rag_documents", {
    "collection_name": "company_docs",
    "query": "quarterly results",
    "n_results": 5
})

Shopify E-commerce API

# Search products
await client.call_tool("search_products", {
    "query": "wireless headphones",
    "limit": 10
})

# Add to cart and checkout
await client.call_tool("add_to_cart", {
    "product_id": "12345",
    "quantity": 2
})

await client.call_tool("start_checkout", {
    "cart_id": "cart_123"
})

Memory Management API

# Store information with categories
await client.call_tool("remember", {
    "key": "customer_preferences",
    "value": "Prefers email notifications",
    "category": "customer_data",
    "keywords": ["email", "notifications", "preferences"]
})

# Search memories
await client.call_tool("search_memories", {
    "query": "customer email preferences"
})

🧪 Testing & Quality Assurance

Comprehensive Test Suite

The project includes 40+ test files covering all major components:

# Run all tests
pytest

# Run specific service tests
pytest tests/test_data_analytics_service.py
pytest tests/test_web_services.py
pytest tests/test_rag_operations.py
pytest tests/test_shopify_integration.py

# Run performance benchmarks
pytest tests/test_performance.py -v

Test Coverage Areas

• ✅ Data Analytics: Complete 5-step workflow testing
• ✅ Web Services: Scraping, extraction, and anti-detection
• ✅ RAG & Documents: Multi-format processing and Q&A
• ✅ E-commerce: Shopping cart and checkout workflows
• ✅ Security: Authentication, authorization, and audit logging
• ✅ AI Features: Tool selection and auto-discovery
• ✅ Integration: End-to-end service interactions

Performance Metrics

• AI Tool Selection: <200ms response time
• Web Scraping Success Rate: 95%+
• Database Query Performance: <500ms average
• Memory Usage per Container: ~500MB
• Docker Container Startup: <30 seconds

📝 Development Standards

• Code Quality: PEP 8 compliance with type annotations
• Testing: Comprehensive unit and integration tests
• Architecture: Async-first with proper error handling
• Security: Multi-level authorization and audit logging
• Documentation: Complete docstrings and API documentation
• Performance: Optimized for production workloads

🔧 Troubleshooting

Common Issues

1. Database Connection Errors

# Check PostgreSQL status
docker-compose ps postgres

# Verify pgvector extension
psql -h localhost -U postgres -c "SELECT * FROM pg_extension WHERE extname='vector';"

2. AI Tool Selection Issues

# Check if embeddings are properly generated
python -c "from core.auto_discovery import AutoDiscovery; print(AutoDiscovery().get_tool_embeddings())"

3. Web Scraping Failures

# Check Playwright browser installation
playwright install chromium

# Verify anti-detection settings
python -c "from tools.services.web_services.browser_manager import BrowserManager; BrowserManager().test_stealth()"

4. Memory Issues in Production

# Monitor container memory usage
docker stats

# Check Redis cache status
redis-cli ping

🚀 Deployment Options

Development (Local)

# Single server for development
python smart_mcp_server.py

# Access at http://localhost:4321

Production (Docker Cluster)

# Start complete cluster with load balancer
docker-compose up -d

# 3 Smart MCP servers + Nginx load balancer
# Access at http://localhost:8081

Cloud Deployment (Railway)

# One-click deployment to Railway
railway up

# Automatic SSL, scaling, and monitoring
# Cost: $10-25/month

Manual Docker Build

# Build production image
docker build -f Dockerfile.production -t isa-mcp:latest .

# Run with environment variables
docker run -d \
  -p 4321:4321 \
  -e DATABASE_URL=postgresql://... \
  -e REDIS_URL=redis://... \
  isa-mcp:latest

📊 Performance & Monitoring

Health Monitoring

# Check cluster health
curl http://localhost:8081/health

# Individual server health
curl http://localhost:4321/health
curl http://localhost:4322/health
curl http://localhost:4323/health

Metrics Collection

• Prometheus: Metrics collection and alerting
• Grafana: Performance dashboards
• Audit Logging: Security and compliance tracking
• Load Balancer Stats: Request distribution and response times

Production Readiness Checklist

• ✅ Docker cluster with load balancing
• ✅ Health monitoring and automatic failover
• ✅ Security: JWT auth, bcrypt passwords, audit logs
• ✅ Performance: <200ms AI tool selection, 95%+ uptime
• ✅ Testing: 40+ test files, integration tests
• ✅ Documentation: Complete API docs and deployment guides

🌟 Key Differentiators

1. AI-Powered Intelligence: Unlike traditional MCP servers, provides intelligent tool selection
2. Enterprise Architecture: Production-grade cluster deployment with monitoring
3. Comprehensive Integration: 35+ tools across 11 service categories
4. Modern Web Platform: Advanced scraping with anti-detection capabilities
5. Extensible Design: Modular architecture for easy service addition

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Setup

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests before submitting
pytest

# Run linting
flake8 --config .flake8
black --check .

🔗 Links & Resources

• MCP Protocol: Model Context Protocol Specification
• FastMCP: FastMCP Documentation
• Supabase: PostgreSQL with pgvector
• Railway: Railway Deployment Platform

📄 License

MIT License - see LICENSE file for details.

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Status: 🟢 Production Ready | Version: 2.0.0 | Last Updated: 2024-12-30