fashion-mcp-server
A fashion vertical MCP server providing tools for product SEO audits and trend analysis to integrate with AI agents like Claude, Cursor, and Codex.
README
Fashion MCP Server
Fashion vertical MCP server. Connect your AI agent (Claude Code, Cursor, Codex) to specialized fashion tools: product SEO audits, trend analysis, competitor monitoring, and seasonal content generation.
Built by alexgenovese.com — fashion vertical MCP for brands, agencies, and creators.
Tools
| Tool | Description |
|---|---|
product_seo_audit |
Full SEO audit of a fashion product page: meta title/description, Product JSON-LD schema, image alt text, URL structure, fashion-specific keywords (size, fit, material, color), and seasonal context. Score 0-100 with actionable recommendations. |
fashion_trend_analysis |
Trend analysis by category (denim, sneakers, bags, dresses). Returns trending keywords, colors with hex codes, silhouette trends, price tier demand, and strategic insights. |
Quickstart
npm install
npm run build
node dist/index.js
Use with Claude Code
claude mcp add fashion -- node /path/to/fashion-mcp-server/dist/index.js
Then in chat:
Run a product SEO audit on "Black Leather Jacket" — url: https://mystore.com/products/black-leather-jacket, category: Outerwear, brand: Acne Studios
Use with Cursor
Add to .cursor/mcp.json:
{
"mcpServers": {
"fashion": {
"command": "node",
"args": ["/path/to/fashion-mcp-server/dist/index.js"]
}
}
}
Examples
1. Product SEO Audit for a New Collection
Ask your AI agent:
Audit "Linen Blend Midi Dress" — price: $189, category: Dresses, brand: Mango, material: linen, color: cream. We're launching this for Summer 2025. Check if the title and description are SEO-optimized.
What happens: The tool checks meta title length (30-60 chars), meta description (120-158 chars), product schema completeness, fashion keyword coverage (size, fit, material, color), and seasonal alignment. Returns a score and prioritized fixes.
2. Trend Research for Seasonal Buying
Ask your AI agent:
What denim trends should I stock for this Fall? I run a contemporary denim brand and need to decide between wide-leg, barrel, and skinny fits.
What happens: The trend analysis tool returns trending keywords ("wide leg jeans" ↑, "barrel jeans" rapidly growing), trending colors (Indigo, Stone Wash, Black), silhouette shifts, price tier demand, and strategic insights (e.g., "Barrel/wide leg is the most important denim silhouette shift since 2015").
3. Full Competitive Intelligence Workflow
Ask your AI agent:
I'm launching a sneaker brand. Analyze the current sneaker market trends and audit our first product page for SEO.
What happens: Two tools fire in sequence — first fashion_trend_analysis maps the sneaker market (retro runners rising, chunky peaking, gorpcore growing, price sweet spot $100-200), then product_seo_audit checks your launch page for optimization gaps. Combined output gives you market positioning + page-level fixes.
Configuration
Copy .env.example to .env and configure optional API keys:
| Variable | Required | Description |
|---|---|---|
SHOPIFY_STORE |
No | Shopify store domain |
SHOPIFY_ACCESS_TOKEN |
No | Admin API access token |
GSC_CLIENT_EMAIL |
No | Search Console service account email |
GSC_PRIVATE_KEY_PATH |
No | Path to JSON private key |
GSC_SITE_URL |
No | Search Console property URL |
Development
npm run dev # Dev mode with hot reload
npm run typecheck # Type checking
npm run build # Production build
Skill Pack (Playbooks)
This repo also includes 12 markdown playbooks in fashion-mcp-skills/skills/ that orchestrate existing MCP servers (Shopify, Meta Ads, GA4, Search Console, etc.) into fashion-specific workflows:
| Playbook | What it does |
|---|---|
seo-audit-fashion |
Full SEO audit for fashion e-commerce |
competitor-intelligence |
Competitor analysis in 10 minutes |
trend-report |
Trend research for seasonal collections |
ad-copy-fashion |
Fashion ad copy generation |
full-store-audit |
360° fashion store audit |
inventory-health-check |
Sell-through, stockout risk, markdown alerts |
competitor-pricing-audit |
Competitor price comparison |
seasonal-drop-planner |
Seasonal drop planning |
email-campaign-fashion |
Fashion email campaigns |
social-content-calendar |
Fashion social content planning |
product-launch-checklist |
Fashion product launch checklist |
brand-visibility-llm |
GEO/AI search visibility score |
Marketplace
Published on:
See docs/MARKETPLACE_DEPLOYMENT.md for deployment instructions.
Structure
src/ # MCP server (Node/TypeScript)
├── index.ts # MCP server entry point
├── types/fashion.ts # Type definitions
├── tools/
│ ├── index.ts # Tool registry
│ ├── product-seo-audit.ts # Product SEO audit
│ └── fashion-trend-analysis.ts # Trend analysis
└── integrations/
└── shopify.ts # Shopify API client
fashion-mcp-skills/ # Skill pack (12 playbook markdowns)
├── skills/ # 12 playbook .md files
├── docs/ # MCP directory, architecture
├── README.md
└── CLAUDE.md
docs/ # Project docs
├── ROADMAP.md # Pain-shaped roadmap
├── PAIN-MATRIX.md # Pain analysis matrix
├── ARCHITECTURE.md # MCP server architecture
└── MARKETPLACE_DEPLOYMENT.md # Publishing guide
License
MIT
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