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FindMine Shopping Stylist: AI-Powered Styling & Conversion Boost - MCP Implementation

FindMine Shopping Stylist: AI-Powered Styling & Conversion Boost

FindMine Shopping Stylist: MCP-powered AI tailors unique styles in real-time, boosting conversions with data-driven precision—your shoppers’ new favorite virtual stylist." )

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Ranked in the top 1% of all AI tools in its category

About FindMine Shopping Stylist

What is FindMine Shopping Stylist: AI-Powered Styling & Conversion Boost?

FindMine Shopping Stylist is an AI-driven platform designed to enhance e-commerce experiences through advanced product styling recommendations and data-driven conversion optimization. It integrates with the FindMine API and third-party systems like Claude to provide real-time styling suggestions, visual search capabilities, and personalized recommendations. The solution empowers businesses to boost customer engagement and sales by leveraging machine learning algorithms that analyze product attributes, user preferences, and contextual data.

How to Use FindMine Shopping Stylist: AI-Powered Styling & Conversion Boost?

  1. Install via npm: npm install findmine-stylist
  2. Configure environment variables (API keys, endpoints)
  3. Implement API calls using predefined handlers:
    • get_style_guide() for styling guidelines
    • get_complete_the_look() for product ensemble suggestions
    • get_visually_similar() for visual search results
  4. Integrate with frontend interfaces using returned JSON data structures

FindMine Shopping Stylist Features

Key Features of FindMine Shopping Stylist: AI-Powered Styling & Conversion Boost?

Smart Styling Engine

Generates outfit recommendations based on product color, material, and seasonal trends

Visual Search Integration

Returns products with similar visual characteristics using neural network analysis

Contextual Recommendations

Adapts suggestions based on user behavior, device type, and purchase history

Customizable Workflow

Modify styling logic through the get_style_guide() handler and TypeScript interfaces

Use Cases of FindMine Shopping Stylist: AI-Powered Styling & Conversion Boost?

  • E-commerce Optimization: Enhance product pages with "Complete the Look" sections
  • Personalization: Create user-specific styling guides during checkout
  • Visual Search: Power "Find Similar Items" features in retail apps
  • Inventory Cross-Selling: Identify complementary products for upselling strategies
  • Seasonal Campaigns: Auto-generate trend-based outfit suggestions for promotions

FindMine Shopping Stylist FAQ

FAQ from FindMine Shopping Stylist: AI-Powered Styling & Conversion Boost?

How does the styling engine prioritize recommendations?

Uses a combination of collaborative filtering, image similarity scores, and real-time trend data weighted by business rules

What API credentials are required?

Only FindMine API access token - no third-party keys needed

Can I customize styling rules?

Yes - modify the src/index.ts handler logic and type definitions

Supported platforms?

Works with Node.js 16+, React, Vue, and any REST API compatible system

How is performance scaled?

Rate-limited API calls with caching strategies in the src/services/ layer

Content

FindMine Shopping Stylist

A Model Context Protocol (MCP) server that integrates FindMine's powerful product styling and outfitting recommendations with Claude and other MCP-compatible applications.

Overview

This MCP server connects to FindMine's styling API and exposes its functionality to Large Language Models through the Model Context Protocol. It allows users to:

  • Browse product and outfit information
  • Get outfit recommendations for specific products
  • Find visually similar products
  • Access style guidance and fashion advice

Features

Resources

  • Products : Detailed product information with product:/// URI scheme
  • Looks : Complete outfit recommendations with look:/// URI scheme

Tools

  • get_style_guide : Access detailed fashion advice and styling guidelines
  • get_complete_the_look : Get outfit recommendations for a product
  • get_visually_similar : Find visually similar products

Prompts

  • outfit_completion : Get styling advice for complete outfits
  • styling_guide : Access comprehensive fashion styling guidelines
  • findmine_help : Learn how to use FindMine's tools and resources

Installation

Option 1: Install from npm

# Install and run directly (recommended)
npx findmine-mcp

# Or install globally
npm install -g findmine-mcp
findmine-mcp

Option 2: Run with Docker

docker run -e FINDMINE_APP_ID=your_app_id findmine/mcp-server:latest

Option 3: Clone and build from source

# Clone the repository
git clone https://github.com/findmine/findmine-mcp.git
cd findmine-mcp

# Install dependencies
npm install

# Build the server
npm run build

# For development with auto-rebuild
npm run watch

Configuration

Environment Variables

Variable Description Default
FINDMINE_API_URL FindMine API base URL https://api.findmine.com
FINDMINE_APP_ID Your FindMine application ID DEMO_APP_ID
FINDMINE_API_VERSION API version to use v3
FINDMINE_DEFAULT_REGION Default region code us
FINDMINE_DEFAULT_LANGUAGE Default language code en
FINDMINE_CACHE_ENABLED Enable response caching true
FINDMINE_CACHE_TTL_MS Cache time-to-live in ms 3600000 (1 hour)
NODE_ENV Set to "development" for sample data -

Usage with Claude Desktop

The server automatically configures Claude Desktop during installation. To verify:

macOS:

cat ~/Library/Application\ Support/Claude/claude_desktop_config.json

Windows:

type %APPDATA%\Claude\claude_desktop_config.json

Development

MCP Inspector

The MCP Inspector is a development tool for testing your server:

npm run inspector

This will open a web interface at http://localhost:5173 where you can interact with your server.

Development Mode

Run the server with sample data:

NODE_ENV=development npm run build && node build/index.js

Customizing the Style Guide

The style guide can be customized to match your brand's specific styling philosophies and fashion guidance. To customize the style guide:

  1. Locate the style guides in src/index.ts (search for styleGuides)
  2. Modify the content for each category (general, color_theory, body_types, etc.)
  3. Add new categories by extending the styleGuides object
  4. Customize occasion-specific and seasonal advice

Example of adding a custom style guide category:

// In src/index.ts
const styleGuides: Record<string, string> = {
  // Existing categories...
  
  // Add your custom category
  your_brand_style: `# Your Brand Style Guide
  
## Brand Aesthetic
- Key elements of your brand's visual identity
- Core style principles
- Signature looks and combinations

## Your Brand's Styling Do's
- Brand-specific styling recommendations
- Preferred color combinations
- Signature styling techniques

## Your Brand's Styling Don'ts
- Combinations to avoid
- Styling approaches that don't align with brand identity
- Common styling mistakes to avoid
`
};

For complete customization, you can modify the entire get_style_guide handler in src/index.ts.

Project Structure

  • src/index.ts: Main MCP server implementation
  • src/api/: FindMine API client
  • src/services/: Business logic and service layer
  • src/types/: TypeScript type definitions
  • src/utils/: Utility functions and helpers

API Examples

Get Style Guide

{
  "name": "get_style_guide",
  "arguments": {
    "category": "color_theory",
    "occasion": "wedding"
  }
}

Get Complete the Look

{
  "name": "get_complete_the_look",
  "arguments": {
    "product_id": "P12345",
    "product_color_id": "C789"
  }
}

Get Visually Similar Products

{
  "name": "get_visually_similar",
  "arguments": {
    "product_id": "P12345",
    "product_color_id": "C789",
    "limit": 5
  }
}

Publishing

Publishing to npm

# Login to npm
npm login

# Publish the package
npm publish

# Update the version for future releases
npm version patch

Publishing to Docker Hub

# Build the Docker image
docker build -t findmine/mcp-server:latest .

# Login to Docker Hub
docker login

# Push the image
docker push findmine/mcp-server:latest

License

This project is licensed under the MIT License.

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