<?xml version="1.0"?>
<rss version="2.0"><channel><title>All Activity</title><link>https://www.clicshopping.org/forum/discover/</link><description>Clicshopping AI, Free generative artificial intelligence e-commerce Open Source , Open Source Store Onlne - All Activity</description><language>en</language><item><title>ClicShoppingAI: Native Agentic E-Commerce Solution</title><link><![CDATA[https://www.clicshopping.org/forum/topic/5475-clicshoppingai-native-agentic-e-commerce-solution/?do=findComment&comment=9902]]></link><description>ClicShopping strengthens its AI ecosystem through MCP compatibility with LmStudio
 


	 
	ClicShopping announces its compatibility with LmStudio through MCP (Model Context Protocol). This integration allows users to select a chat directly within LmStudio and initiate a conversation with ClicShopping without accessing the ClicShopping interface.
 


	 
 


	The integration simplifies the use of local AI models while maintaining centralized control. New features can be created easily, with all governance, business rules, and permissions fully managed by ClicShopping. LmStudio functions solely as a conversational access point, without duplicating application logic.
 


	An example image below illustrates a response generated by LmStudio in this integrated context.
 


	 
	What is MCP? 
	 
 


	MCP (Model Context Protocol) is a standardized protocol that enables external applications to interact with structured services or models while delegating business logic, contextual data, and governance to a dedicated system. It enforces a clear separation between conversational interfaces and application backends.
 


	 
	Why use MCP with ClicShopping?
 


	
		Direct access to ClicShopping capabilities from local AI environments such as LmStudio
	
	
		Centralized governance, security, and business logic managed by ClicShopping
	
	
		Rapid feature development without modifying AI client applications
	
	
		Reduced integration complexity and improved maintainability
	
	
		Support for advanced use cases combining e-commerce and conversational AI
	



	This compatibility example demonstrates ClicShopping&#x2019;s strategy to build an open, interoperable AI ecosystem while retaining full control over its platform and governance model.</description><pubDate>Tue, 24 Feb 2026 11:17:48 +0000</pubDate></item><item><title>ClicShoppingAI: Native Agentic E-Commerce Solution</title><link><![CDATA[https://www.clicshopping.org/forum/topic/5475-clicshoppingai-native-agentic-e-commerce-solution/?do=findComment&comment=9886]]></link><description>We are proud now to announce you ClicShoppingAI is a native agentic e-commerce platform built on an open, multi-agent architecture orchestrated by a central Orchestrator Agent. Designed for extensibility, the platform enables the dynamic addition of new agents and functional domains as business needs evolve. 
	 
 


	Multi-Agent Architecture
 


	 
 


	At the core of the system, the Orchestrator Agent analyzes user intent and routes requests to the appropriate domain agents. Specialized agents operate across key functions:
 


	Semantic Agent for semantic search and content understanding using vector embeddings and retrieval-augmented generation (RAG).
 


	Analytics Agent for internal data analysis, automated SQL generation, and business intelligence.
 


	WebSearch Agent for retrieving external information from the public web and competitive sources.
 


	Hybrid Agent combining semantic, analytics, and web search capabilities to handle complex queries.
 


	 
 


	Conversational Interface
 


	ClicShoppingAI features a conversational chat interface that supports natural language queries and delivers contextualized responses through intelligent intent analysis, contextual awareness, response validation, and multilingual support.
 


	Monitoring and Performance
 


	 
 


	A centralized dashboard provides real-time visibility into platform activity with key performance indicators, including agent performance metrics, system latency, cache usage, alerts, trends, and token consumption statistics.
 


	Extensible by Design
 


	 
 


	The platform relies on standardized interfaces and a clear three-layer architecture (Domains, Apps, Agents), ensuring clean separation between query processing, business logic, and autonomous agent behavior. This design allows rapid integration of new agents and domains without disrupting existing functionality.
 


	 
	An example below : 
	 
	
 


	 
 


	 
 


	An request example across the chat :</description><pubDate>Sun, 25 Jan 2026 22:46:18 +0000</pubDate></item><item><title>Model Context Protocol (MCP)</title><link><![CDATA[https://www.clicshopping.org/forum/topic/5474-model-context-protocol-mcp/?do=findComment&comment=9867]]></link><description><![CDATA[ClicShopping Version 4.08 and more :
 


	MCP (Model Context Protocol) Documentation for ClicShopping



	Overview



	The ClicShopping MCP (Model Context Protocol) system allows for the integration of external Node.js or Python servers to extend the e-commerce application’s capabilities with advanced Artificial Intelligence functionalities. It provides a modular architecture for communication between ClicShopping and external services via standardized protocols.
 


	What is MCP?



	MCP is a communication protocol that enables applications to interact with language models and AI services in a standardized manner. In the context of ClicShopping, it facilitates:
 


	
		Bidirectional communication between the e-commerce application and external AI servers
	
	
		Integration of intelligent agents for task automation
	
	
		Data access via secured REST APIs
	
	
		Real-time monitoring and analytics of interactions
	



	Importance in E-commerce



	Advantages:



	
		Intelligent automation: Automatic order management, product recommendations
	
	
		24/7 customer support: Smart chatbots for customer assistance
	
	
		Advanced analytics: Predictive analysis of sales and customer behavior
	
	
		Personalization: AI-based personalized recommendations
	
	
		Inventory optimization: Demand forecasting and automatic management
	



	Disadvantages:



	
		Implementation complexity: Requires advanced technical skills
	
	
		Infrastructure costs: External servers and AI services
	
	
		External dependency: Risk of third-party service outages
	
	
		Security: Management of tokens and secure access
	



	Examples of potential implementations:



	🔗 Social Media Integrations



	
		Instagram Shopping: Automatic product synchronization with Instagram posts
	
	
		Facebook Marketplace: Automatic publication of new products
	
	
		TikTok Shop: Integration with TikTok trends for recommendations
	
	
		Pinterest: Automatic creation of pins for popular products
	



	🏢 ERP Integrations



	
		SAP: Synchronization of stocks, orders, and customers
	
	
		Oracle NetSuite: Accounting integration and financial management
	
	
		Microsoft Dynamics: Synchronization of customer and sales data
	
	
		Odoo: Full CRM/ERP integration with inventory management
	



	📈 Marketing Integrations



	
		Mailchimp: Automatic customer segmentation and targeted campaigns
	
	
		HubSpot: Lead scoring and customer journey automation
	
	
		Google Analytics 4: Advanced tracking of conversions and behavior
	
	
		Facebook Ads: Automatic optimization of advertising campaigns
	



	💳 Payment Integrations



	
		Stripe: Management of subscriptions and recurring payments
	
	
		PayPal: Integration of payments and refunds
	
	
		Klarna: Installment payments and credit scoring
	
	
		Apple Pay/Google Pay: Optimized mobile payments
	



	📦 Logistics Integrations



	
		DHL/UPS/FedEx: Automatic shipping cost calculation and tracking
	
	
		Amazon FBA: Amazon stock management and synchronization
	
	
		Shopify Fulfillment: Optimization of distribution centers
	
	
		ShipStation: Multi-carrier shipping automation
	



	🎯 Analytics &amp; BI Integrations



	
		Tableau: Advanced sales dashboards
	
	
		Power BI: Predictive analytics and automated reports
	
	
		Google Data Studio: Marketing and performance reporting
	
	
		Mixpanel: Advanced user event tracking
	



	🤖 AI &amp; Chatbot Integrations



	
		OpenAI GPT: Smart chatbot for customer support
	
	
		Dialogflow: Multilingual conversation management
	
	
		Zendesk: Automation of support tickets
	
	
		Intercom: Real-time chat with lead qualification
	



	📱 Mobile Integrations



	
		React Native: Native mobile application
	
	
		Flutter: Cross-platform iOS/Android app
	
	
		PWA: Progressive Web Application
	
	
		Push Notifications: Personalized notifications
	



	🔐 Security Integrations



	
		Auth0: Advanced authentication and authorization
	
	
		Okta: Identity and access management
	
	
		Cloudflare: DDoS protection and CDN
	
	
		Sentry: Real-time error monitoring
	



	Examples of Integration Code



	Example 1: Instagram Shopping Integration


// New MCP endpoint for Instagram
class InstagramIntegration extends \ClicShopping\OM\PagesAbstract
{
    public function syncProductsToInstagram(): void
    {
        $products = $this-&gt;getProductsForSync();
        
        foreach ($products as $product) {
            $instagramData = [
                'name' =&gt; $product['products_name'],
                'description' =&gt; $product['products_description'],
                'price' =&gt; $product['products_price'],
                'image_url' =&gt; $product['products_image'],
                'availability' =&gt; $product['products_quantity'] &gt; 0 ? 'in stock' : 'out of stock'
            ];
            
            $this-&gt;postToInstagramAPI($instagramData);
        }
    }
}


	Example 2: SAP ERP Integration


// Synchronization with SAP via MCP
class SAPIntegration extends \ClicShopping\OM\PagesAbstract
{
    public function syncOrdersToSAP(): void
    {
        $orders = $this-&gt;getPendingOrders();
        
        foreach ($orders as $order) {
            $sapData = [
                'order_number' =&gt; $order['orders_id'],
                'customer_code' =&gt; $order['customers_id'],
                'order_date' =&gt; $order['date_purchased'],
                'items' =&gt; $this-&gt;formatOrderItems($order['items'])
            ];
            
            $response = $this-&gt;sendToSAP($sapData);
            $this-&gt;updateOrderStatus($order['orders_id'], $response['status']);
        }
    }
}


	Example 3: AI Chatbot with OpenAI


// Smart chatbot for customer support
class AIChatbot extends \ClicShopping\OM\PagesAbstract
{
    public function handleCustomerInquiry(string $message): array
    {
        $context = $this-&gt;getCustomerContext();
        
        $prompt = "As an e-commerce assistant, help this customer: " . $message;
        $prompt .= "\nCustomer context: " . json_encode($context);
        
        $response = $this-&gt;callOpenAI($prompt);
        
        // If necessary, create a support ticket
        if ($this-&gt;requiresHumanIntervention($response)) {
            $this-&gt;createSupportTicket($message, $context);
        }
        
        return [
            'response' =&gt; $response,
            'requires_human' =&gt; $this-&gt;requiresHumanIntervention($response),
            'suggested_products' =&gt; $this-&gt;extractProductSuggestions($response)
        ];
    }
}


	Example 4: Predictive Analytics


// Sales prediction with AI
class PredictiveAnalytics extends \ClicShopping\OM\PagesAbstract
{
    public function predictSales(): array
    {
        $historicalData = $this-&gt;getSalesHistory();
        $externalFactors = $this-&gt;getExternalData(); // Weather, events, etc.
        
        $prediction = $this-&gt;runMLModel([
            'historical_sales' =&gt; $historicalData,
            'seasonality' =&gt; $this-&gt;getSeasonalityFactors(),
            'external_factors' =&gt; $externalFactors,
            'inventory_levels' =&gt; $this-&gt;getCurrentInventory()
        ]);
        
        return [
            'predicted_sales' =&gt; $prediction['sales'],
            'recommended_stock' =&gt; $prediction['stock_recommendations'],
            'confidence_score' =&gt; $prediction['confidence'],
            'risk_factors' =&gt; $prediction['risks']
        ];
    }
}


	Concrete Use Cases



	🛒 B2C E-commerce



	
		Personalized recommendations: “Customers who bought this product also viewed…”
	
	
		24/7 chat support: Automatic assistance with escalation to a human
	
	
		Intelligent inventory management: Stock-out prediction
	
	
		Dynamic pricing: Automatic price adjustment based on competition
	



	🏢 B2B E-commerce



	
		Personalized catalog: Prices and products according to the customer
	
	
		Large order management: ERP integration for high volumes
	
	
		Automated reporting: Dashboards for resellers
	
	
		Discount management: Automatic calculation based on commercial agreements
	



	🎯 Marketplace



	
		Multi-vendor synchronization: Centralized stock management
	
	
		Fraud detection: Automatic detection of suspicious transactions
	
	
		Fee optimization: Automatic commission calculation
	
	
		Dispute management: Automation of resolution processes
	



	📱 Mobile Commerce



	
		Smart push notifications: Personalized notifications
	
	
		Geolocation: Location-based offers
	
	
		Mobile payments: Apple Pay/Google Pay integration
	
	
		Image recognition: Product search by photo
	



	Measurable Business Benefits



	📈 Sales Performance



	
		+25% conversion thanks to personalized recommendations
	
	
		-40% shopping cart abandonment with the smart chat
	
	
		+30% average cart value via cross-sell suggestions
	
	
		-60% order processing time with automation
	



	💰 Cost Optimization



	
		-50% support costs with chat automation
	
	
		-30% logistics costs with inventory optimization
	
	
		-25% marketing costs with precise targeting
	
	
		-70% human errors with process automation
	



	🎯 Customer Experience



	
		+90% customer satisfaction with 24/7 support
	
	
		-80% response time to customer inquiries
	
	
		+45% loyalty thanks to personalization
	
	
		+60% problem resolution rate on first interaction
	




	MCP Architecture



	General Architecture


┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   ClicShopping  │◄──►│    MCP Server   │◄──►│    AI Services  │
│   (PHP Core)    │    │  (Node.js/Python)│    │   (OpenAI, etc.)│
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│     Database    │    │   Monitoring    │    │    Analytics    │
│                 │    │     &amp; Logs      │    │    &amp; Reports    │
└─────────────────┘    └─────────────────┘    └─────────────────┘



	ClicShopping MCP Architecture



	The ClicShopping MCP system is organized into several components:
 


	1. Core Classes



	
		MCPConnector: Connection and protocol management
	
	
		McpMonitor: Performance oversight and monitoring
	
	
		McpService: Core services for MCP operations
	
	
		McpDecisionAgent: Intelligent agent for automation
	



	2. API Endpoints



	
		/mcp&amp;customersProducts: Products API
	
	
		/mcp&amp;ragBI: RAG (Retrieval-Augmented Generation) Interface for admins - the rag must ve activated
	
	
		You are free to create other EndPoints (see example above)
	



	3. Admin Configuration



	
		Administration interface for configuring MCP servers
	
	
		Token, port, SSL management
	
	
		Real-time monitoring
	



	What is Not Provided with the APP



	The Chat



	The chat interface is not included in the ClicShopping application. To implement it:
 


	Chat construction and connection:



	
		Create a chat interface (HTML/CSS/JavaScript)
	
	
		Connect to the MCP server via WebSocket or HTTP
	
	
		Use the available API endpoints:
		// Example of chat connection
const chatEndpoint = 'http://your-domain/index.php?mcp&amp;customersProducts';

// Sending a message
fetch(chatEndpoint, {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer ' + token
  },
  body: JSON.stringify({
    message: 'Looking for products',
    context: {
      user_type: 'client',
      session_id: 'unique_session_id'
    }
  })
});

		
			 
		 
	



	The Node.js/Python MCP Server



	The external MCP server is not provided with the application. To build it:
 


	Node.js MCP Server Example:


// Example of a Node.js MCP server
const express = require('express');
const app = express();

app.post('/mcp/products', async (req, res) =&gt; {
  // Logic for processing product requests
  const response = await processProductRequest(req.body);
  res.json(response);
});

app.listen(3000, () =&gt; {
  console.log('MCP server started on port 3000');
});


	Server Connection:



	
		Configuration in the ClicShopping admin: Host, Port, SSL, Token
	
	
		API Usage: Access via the /Shop/json routes
	




	Configuration and Usage



	Configuration in the Administration



	MCP configuration is done directly in the ClicShopping administration interface:
 


	Available parameters:



	
		Server Host: Address of the MCP server (default: localhost)
	
	
		Server Port: Port of the MCP server (default: 3000)
	
	
		SSL: Secure protocol activation
	
	
		Token: Authentication token for security
	
	
		Status: MCP module activation/deactivation
	



	Alert configuration:



	
		Latency thresholds: Maximum response time
	
	
		Availability thresholds: Maximum downtime
	
	
		Notifications: Email alert configuration
	



	Access API - Shop Routes



	The MCP API is accessible via the /Shop/json routes of ClicShopping:
 


	1. CustomersProducts.php



	This class serves as the main entry point for the MCP products API. It manages:
 


	Main features:
 


	
		Product list: GET ?mcp&amp;customersProducts&amp;action=products
Product detail: GET ?mcp&amp;customersProducts&amp;action=product&amp;id={ID}
Search: GET ?mcp&amp;customersProducts&amp;action=search&amp;query={TERM}
Statistics: GET ?mcp&amp;customersProducts&amp;action=stats
Categories: GET ?mcp&amp;customersProducts&amp;action=categories
Recommendations: GET ?mcp&amp;customersProducts&amp;action=recommendations
Customer chat: POST ?mcp&amp;customersProducts (with JSON body)

		
			 
		 
	
	
		 
	



	Example usage:
 

# Product list
curl "http://localhost/clicshopping_test/index.php?mcp&amp;customersProducts&amp;action=products&amp;limit=5"

# Product search
curl "http://localhost/clicshopping_test/index.php?mcp&amp;customersProducts&amp;action=search&amp;query=washcloth"

# Customer chat (POST)
curl -X POST "http://localhost/clicshopping_test/index.php?mcp&amp;customersProducts" \
  -H "Content-Type: application/json" \
  -d '{"message": "I am looking for cleaning products", "context": {"user_type": "client"}}'


	2. RagBI.php



	RAG (Retrieval-Augmented Generation) interface identical to ClicShopping’s internal chat but accessible via MCP: 
	To use it, you must activate the Agent RAG-BI inside the administration.
 


	Features:
 


	
		Semantic queries: Smart search in the database
	
	
		Analytical queries: Analysis of sales and performance data
	
	
		OpenAI Integration: Use of language models for responses
	
	
		Translation cache: Performance optimization
	



	Example usage:
 

# RAG BI Query
curl -X POST "http://localhost/clicshopping_test/index.php?mcp&amp;ragBI" \
  -H "Content-Type: application/json" \
  -d '{"message": "Give me a table of the evolution of turnover by month for the year 2025"}'


	3. customerOrders.php



	Customer order management API:
 


	Features:
 


	
		Order list: GET ?mcp&amp;customerOrders&amp;action=list_orders&amp;customer_id={ID}
Order detail: GET ?mcp&amp;customerOrders&amp;action=read_order&amp;order_id={ID}
Cancellation: POST ?mcp&amp;customerOrders&amp;action=cancel_order
Messages: POST ?mcp&amp;customerOrders&amp;action=send_message
History: GET ?mcp&amp;customerOrders&amp;action=history&amp;order_id={ID}

		
			 
		 
	
	
		 
	



	Examples of Future Implementation



	Agentic Approach



	The MCP system supports the implementation of intelligent agents for:
 


	
		
			Recommendation Agent:
		 

		// Example of a Recommendation Agent
class RecommendationAgent {
  public function analyzeCustomerBehavior($customerId) {
    // Analyze customer behavior
    // Generate personalized recommendations
  }
}

		
			 
		 
	
	
		
			Stock Management Agent:
		 

		// Example of a Stock Management Agent
class StockAgent {
  public function predictDemand($productId) {
    // Demand prediction
    // Optimization of stock levels
  }
}

		
			 
		 
	
	
		
			Customer Support Agent:
		 

		// Example of a Support Agent
class SupportAgent {
  public function handleCustomerInquiry($message) {
    // Process customer inquiries
    // Automatic escalation if necessary
  }
}

		
			 
		 
	




	Monitoring and CronJobs



	Monitoring System



	The MCP system includes complete monitoring:
 


	Monitored metrics:



	
		Response time: Latency of MCP requests
	
	
		Availability: Uptime of the MCP server
	
	
		Errors: Error rate and error types
	
	
		Security: Intrusion attempts and unauthorized access
	



	Automatic alerts:



	
		Performance thresholds: Alerts if response time &gt; threshold
	
	
		Service outages: Notifications in case of unavailability
	
	
		Suspicious activities: Detection of attacks or abuse
	



	CronJob Configuration



	The MCP system uses scheduled tasks for:
 


	1. Health Monitoring (every 5 minutes)


// CronJob: McpHealthCron
// Checks the health of the MCP server
// Stores alerts in the database
// Cleans up old alerts (&gt;30 days)


	2. Decision Agent (every 5 minutes)


// CronJob: mcp_agent
// Executes the intelligent decision agent
// Processes automated tasks
// Updates recommendations


	CronJob Configuration:


 

# Add to crontab
*/5 * * * * /usr/bin/php /path/to/clicshopping/index.php?cronId={CRON_ID}



	Security



	Authentication and Authorization



	Access Tokens:



	
		Secure generation: Unique tokens per session
	
	
		Automatic expiration: Token rotation
	
	
		Validation: Verification on every request
	



	Endpoint protection:



	
		Configured CORS: Controlled access by origin
	
	
		Parameter validation: Input sanitization
	
	
		Production mode: Access restrictions in production
	



	Security Best Practices



	
		Use HTTPS in production
	
	
		Configure strong tokens and renew them regularly
	
	
		Limit access by IP if necessary
	
	
		Monitor logs to detect suspicious activities
	
	
		Regularly update dependencies
	



	Troubleshooting



	Common Problems



	1. Connection to the MCP server fails



	
		Check the configuration (host, port, SSL)
	
	
		Verify that the MCP server is started
	
	
		Check error logs
	



	2. Authentication errors



	
		Verify the token validity
	
	
		Check permission configuration
	
	
		Check security logs
	



	3. Degraded performance



	
		Check monitoring metrics
	
	
		Optimize database queries
	
	
		Increase resource limits
	



	Logs and Debugging



	Log files:



	
		MCP Logs: Available in the database and admin interface (export) for various traceability
	
	
		PHP Error Logs: Standard PHP configuration
	
	
		Monitoring Logs: mcp_alerts database
	




	Support and Resources



	Additional Documentation



	
		DeepWiki/ClicShopping: Detailed architecture : https://deepwiki.com/ClicShopping/ClicShopping
	
	
		GitHub Issues: Technical support and bugs : https://github.com/ClicShopping/ClicShopping/issues
	
	
		ClicShopping Forum: Community and assistance]]></description><pubDate>Mon, 29 Sep 2025 20:35:43 +0000</pubDate></item><item><title><![CDATA[Important information for the next release >4.02]]></title><link><![CDATA[https://www.clicshopping.org/forum/topic/5469-important-information-for-the-next-release-402/?do=findComment&comment=9851]]></link><description>This version move the includes directory to Core directory. If you install an app do not forget to change the includes directory into Core directory. 
	 
	Until the transition is not completed, please do not install directly from the ClicShopping AI an App. Download and do it manually. 
	Thank you.</description><pubDate>Fri, 18 Jul 2025 15:11:12 +0000</pubDate></item><item><title>How to install Clicshopping V3</title><link><![CDATA[https://www.clicshopping.org/forum/topic/294-how-to-install-clicshopping-v3/?do=findComment&comment=8032]]></link><description>good solution dear.</description><pubDate>Thu, 01 May 2025 11:19:48 +0000</pubDate></item></channel></rss>
