tomrikert/wayfair-mcp-server

Wayfair MCP Server Prototype

This prototype demonstrates how retailers can enhance AI agent experiences by providing structured access to their product data through MCP (Model Context Protocol) servers.

Value Proposition

Current State (Without MCP)

When AI agents like ChatGPT try to help users find products on Wayfair:

• Limited to web scraping and general search
• Inconsistent product information
• No access to real-time inventory, pricing, or availability
• Cannot perform actions like adding to cart or checking out
• Poor user experience with incomplete or outdated information

Enhanced State (With MCP Server)

With a custom MCP server for Wayfair:

• Structured, reliable access to product data
• Real-time inventory and pricing information
• Ability to perform actions (search, filter, add to cart)
• Consistent, accurate product recommendations
• Better user experience with complete, up-to-date information

Prototype Components

1. MCP Server (wayfair_mcp_server.py)

• Implements MCP protocol for Wayfair product data
• Provides tools for searching, filtering, and retrieving product information
• Simulates real API endpoints with structured data

2. Demo Scripts

• demo_current_state.py: Shows limitations of current ChatGPT approach
• demo_enhanced_state.py: Demonstrates improved experience with MCP server
• product_data.json: Sample product database for the prototype

3. Configuration

• mcp_config.json: MCP server configuration
• requirements.txt: Python dependencies

Quick Start

1. Install dependencies:

pip install -r requirements.txt

2. Start the MCP server:

python wayfair_mcp_server.py

3. Run the demo comparisons:

python demo_current_state.py
python demo_enhanced_state.py

Use Cases Demonstrated

Product Search

• Current: ChatGPT searches web, gets inconsistent results
• Enhanced: Direct access to structured product catalog with filters

Product Comparison

• Current: Limited to general descriptions
• Enhanced: Detailed specifications, pricing, availability

Shopping Cart Operations

• Current: Cannot perform actions
• Enhanced: Add items, manage cart, checkout process

Inventory Checking

• Current: No real-time data
• Enhanced: Live inventory status and delivery estimates

Business Impact

For Retailers

• Increased Conversion: Better product discovery leads to more sales
• Reduced Support: AI agents can handle customer queries directly
• Competitive Advantage: First-mover advantage in AI agent integration
• Data Control: Maintain control over product presentation and pricing

For Customers

• Better Experience: Accurate, up-to-date product information
• Faster Decisions: Quick access to relevant products
• Reduced Friction: Seamless shopping through AI agents

Technical Implementation

The prototype uses:

• MCP Protocol: Standard interface for AI agent integration
• FastAPI: Modern web framework for the server
• Structured Data: JSON-based product catalog
• Tool Definitions: Clear API for AI agent interactions

Next Steps

1. Real API Integration: Connect to actual Wayfair API endpoints
2. Authentication: Implement secure access controls
3. Scalability: Handle high-volume requests
4. Analytics: Track usage and conversion metrics
5. Multi-Retailer: Extend to support multiple retailers

Files Structure

├── README.md                 # This file
├── wayfair_mcp_server.py     # Main MCP server implementation
├── demo_current_state.py     # Demo of current limitations
├── demo_enhanced_state.py    # Demo of enhanced experience
├── product_data.json         # Sample product database
├── mcp_config.json          # MCP server configuration
└── requirements.txt         # Python dependencies