What can you do with this server?

This server provides MCP tools for fashion e-commerce AI agents to perform SEO audits and trend analysis. product_seo_audit — Audits a fashion product page for SEO best practices, returning: * A score (0–100) * Checks on meta title (ideal: 30–60 chars), meta description (ideal: 120–158 chars), Product JSON-LD structured data, image alt text, URL structure, H1, fashion-specific keywords (size, fit, material, color), and seasonal alignment (e.g., SS25, FW25) * Prioritized, actionable recommendations * Works across any platform (Shopify, Magento, custom) — no API key required fashion_trend_analysis — Analyzes current fashion trends for a product category (denim, sneakers, bags, dresses, or custom), returning: * Trending keywords with search volume direction (up/down/stable) * Trending colors with hex codes and usage context * Silhouette trends labeled as rising/peaking/declining/stable * Price tier demand (entry, mid, premium, luxury) * Strategic key insights for content planning, merchandising, or product development * Filterable by market (US, EU, global), season, and timeframe (1 week to 1 year) * Can accept and enrich data from Tavily MCP for real-time trend intelligence Both tools support verbose and max_words parameters to control output detail and manage token costs. The server also comes with a skill pack of 12 markdown playbooks that orchestrate these tools with other MCP servers (e.g., Shopify, Search Console, Meta Ads) for complex workflows like competitor intelligence, monthly trend reports, and seasonal drop planning.

Which integrations are available for this server?

Integrates with Meta Ads to support fashion ad copy generation and advertising workflows. Integrates with Shopify to enable product SEO audits and trend analysis for fashion e-commerce stores.

How do I use ecommerce-fashion-market-analysis?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@ecommerce-fashion-market-analysis Run a product SEO audit on a black leather jacket" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

ecommerce-fashion-market-analysis

by alexgenovese

Overview Schema Related Servers Score Discussions

TypeScript

Local

E-commerce Fashion Market Analysis

MCP tools for fashion e-commerce AI agents.

SEO audits, trend analysis, competitor monitoring, and seasonal content generation — built for Claude Code, Cursor, Codex, and any MCP-compatible agent.

Smithery GitHub Node.js TypeScript MCP SDK License: MIT

Overview • Quick Start • Tools • Examples • Integration • Skill Pack • Configuration

Overview

E-commerce Fashion Market Analysis is a vertical MCP server that gives your AI agent specialized fashion e-commerce knowledge. It runs locally via stdio — no cloud deployment required.

Built by alexgenovese.com for fashion brands, agencies, and creators.

NOTE

What is MCP? Model Context Protocol is an open standard that lets AI agents call external tools. This server exposes fashion-specific tools that any MCP-compatible client (Claude Code, Cursor, Codex, Gemini CLI) can discover and use.

What it does

Capability	Without MCP	With Fashion MCP
SEO audit	Manual checklist, generic advice	Automated score 0-100 with fashion-specific checks (fit, material, color, season)
Trend research	Generic Google searches	Category-level trend data with keywords, colors, silhouettes, price tiers
Competitor analysis	Hours of manual research	Structured output your agent can act on instantly

Related MCP server: Gadget MCP Server

Quick Start

Install

git clone https://github.com/alexgenovese/ecommerce-fashion-market-analysis.git
cd ecommerce-fashion-market-analysis
npm install
npm run build

Connect to your AI agent

claude mcp add fashion -- node /path/to/ecommerce-fashion-market-analysis/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

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "fashion": {
      "command": "node",
      "args": ["/path/to/ecommerce-fashion-market-analysis/dist/index.js"]
    }
  }
}

Add to your agent's MCP config:

{
  "mcpServers": {
    "fashion": {
      "command": "node",
      "args": ["/path/to/ecommerce-fashion-market-analysis/dist/index.js"]
    }
  }
}

Add to ~/.config/opencode/opencode.json or the project's opencode.json:

{
  "mcp": {
    "fashion": {
      "type": "local",
      "command": ["node", "/path/to/ecommerce-fashion-market-analysis/dist/index.js"],
      "enabled": true
    }
  }
}

Then restart opencode for the changes to take effect.

Add to your VS Code settings.json (Cmd+Shift+P → "Preferences: Open User Settings (JSON)"):

{
  "github.copilot.mcpServers": {
    "fashion": {
      "command": "node",
      "args": ["/path/to/ecommerce-fashion-market-analysis/dist/index.js"]
    }
  }
}

Add to your ~/.continue/config.json:

{
  "experimental": {
    "mcpServers": {
      "fashion": {
        "command": "node",
        "args": ["/path/to/ecommerce-fashion-market-analysis/dist/index.js"]
      }
    }
  }
}

Connect directly without cloning:

{
  "mcpServers": {
    "fashion": {
      "url": "https://ecommerce-fashion-market-analysis--alexgenovese.run.tools"
    }
  }
}

Tools

`product_seo_audit`

Full SEO audit of a fashion product page. Returns a score (0-100) with actionable recommendations.

Checks: meta title length, meta description, Product JSON-LD schema, image alt text, URL structure, fashion keywords (size, fit, material, color), seasonal context alignment.

Input parameters:

Parameter	Required	Description
`title`	Yes	Product title
`url`	No	Full product URL
`description`	No	Meta description or product description
`price`	No	Product price
`category`	No	Product category
`brand`	No	Brand name
`season`	No	Season context (e.g. "SS25", "FW25")
`verbose`	No	Output level: `0` (compact), `1` (default), `2` (full)
`max_words`	No	Maximum words in response (default: 200)

Output: productTitle, url, score, checks[], recommendations[], structuredData, images[]

`fashion_trend_analysis`

Structured trend intelligence from Tavily MCP search data. Accepts results from tavily_search, tavily_search_dedup, and tavily_social_media_search — extracts trending keywords, colors, silhouettes, and key insights.

Returns trending keywords, colors with hex codes, silhouette trends, price tier demand, and strategic insights.

Input parameters:

Parameter	Required	Description
`category`	Yes	Product category (e.g. "denim", "sneakers", "bags")
`season`	No	Season filter
`market`	No	Target market (e.g. "US", "EU", "global")
`search_results`	No	Results array from `tavily_search` / `tavily_search_dedup`
`search_answer`	No	AI-generated answer from Tavily (`include_answer: true`)
`social_results`	No	Results from `tavily_social_media_search`
`verbose`	No	Output level: `0` (compact), `1` (default), `2` (full)
`max_words`	No	Maximum words in response (default: 200)

Output: category, season, market, generatedAt, trendingKeywords[], trendingColors[], silhouettes[], priceRanges[], keyInsights[]

No API key required in this server. Install Tavily MCP (@tavily/mcp) alongside — it handles search, this tool handles fashion analysis.

TIP

Useverbose: 0 to control token cost when running multiple audits. Use verbose: 2 for detailed analysis with full rationale.

Examples

1. Product SEO Audit

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.

What happens: The tool checks meta title length (30-60 chars), meta description (120-158 chars), Product schema completeness, fashion keyword coverage, 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.

What happens: The AI agent calls Tavily MCP (tavily_search) to get real web data, then passes the results to fashion_trend_analysis which extracts structured trend intelligence — keywords, colors, silhouettes, and insights. No simulated data.

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, then product_seo_audit checks your launch page. Combined output gives you market positioning + page-level fixes.

Skill Pack

This repo 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	Problem it solves	MCP servers required
`seo-audit-fashion`	Full SEO audit for fashion e-commerce	Shopify + Search Console + fashion-mcp-server
`competitor-intelligence`	Competitor analysis in 10 minutes	Similarweb + Semrush + Motion + ShopSavvy
`competitor-pricing-audit`	Compare your prices with competitors	Shopify + ShopSavvy + Similarweb
`trend-report`	Monthly trend report by category	fashion-mcp-server + Exa Search + Semrush
`ad-copy-fashion`	Fashion ad copy for FB/IG/TikTok	Shopify + Meta Ads + fashion-mcp-server
`full-store-audit`	360-degree fashion store audit	All of the above
`inventory-health-check`	Sell-through, stockout risk, markdown alerts	Shopify + BigQuery (optional)
`seasonal-drop-planner`	Seasonal drop planning	fashion-mcp-server + Semrush + Meta Ads
`email-campaign-fashion`	Fashion email campaigns	Shopify + Mailchimp/Klaviyo
`social-content-calendar`	Weekly fashion social content	fashion-mcp-server + TikTok + YouTube
`product-launch-checklist`	Pre-launch checklist	Shopify + Search Console
`brand-visibility-llm`	AI search visibility score	Peec AI + Exa Search

IMPORTANT

The skill pack is the primary product for 85% of the market. The MCP server is for early adopters comfortable with MCP setup. The skills work with any MCP-compatible agent — no custom server required.

Install skills

# Claude Code
cp -r fashion-mcp-skills/skills/* ~/.claude/skills/

# Cursor
cp -r fashion-mcp-skills/skills/* ~/.cursor/skills/

# Codex
cp -r fashion-mcp-skills/skills/* "${CODEX_HOME:-$HOME/.codex}/skills/"

Configuration

Copy .env.example to .env and configure the required API key:

Variable	Required	Description
`TAVILY_API_KEY`	Yes (for Tavily MCP)	Required by `@tavily/mcp` (separate server). Not used by this server directly
`SHOPIFY_STORE`	No	Shopify store domain (future integration)
`SHOPIFY_ACCESS_TOKEN`	No	Admin API access token (future integration)
`GSC_CLIENT_EMAIL`	No	Search Console service account email (future)
`GA4_PROPERTY_ID`	No	Google Analytics 4 property ID (future)
`META_ACCESS_TOKEN`	No	Meta Ads access token (future)
`META_AD_ACCOUNT_ID`	No	Meta Ads account ID (future)

IMPORTANT

fashion_trend_analysis requires Tavily MCP (@tavily/mcp) to gather search data first. Install both servers side by side — the AI agent orchestrates: Tavily MCP for search → this server for structured analysis. product_seo_audit works with data you provide directly and does not require any API keys.

Architecture

┌─────────────────────────────────────────────────────────┐
│                    AI Agent                              │
│  (Claude Code, Cursor, Codex, Gemini CLI, opencode)     │
└──────────────────────┬──────────────────────────────────┘
                       │
                       │  MCP stdio (JSON-RPC)
                       │  ListTools / CallTool
                       ▼
┌─────────────────────────────────────────────────────────┐
│                fashion-mcp-server                        │
│                                                         │
│  ┌──────────────────────────────────────────────────┐   │
│  │                Server (index.ts)                  │   │
│  │  ListToolsRequestSchema → discover tools          │   │
│  │  CallToolRequestSchema → route & execute tool     │   │
│  │  Error handling → McpError                       │   │
│  └──────────────┬───────────────────────────────────┘   │
│                  │                                       │
│     ┌────────────┴────────────┐                          │
│     ▼                         ▼                          │
│  ┌──────────────────┐  ┌──────────────────────────┐     │
│  │ product_seo_audit │  │ fashion_trend_analysis   │     │
│  │                   │  │                          │     │
 │  │ Pure validation   │  │ Accepts Tavily MCP data ↓│     │
│  │ of user-provided  │  │ search_results / answer    │     │
│  │ product data      │  │ social_results             │     │
│  │                   │  │ (no HTTP search calls)     │     │
│  │ No API key needed │  │                            │     │
│  └──────────────────┘  └──────────────────────────────┘     │
│                                                             │
│  ┌──────────────────────────────────────────────────────┐   │
│  │    External MCP Servers (run side by side)            │   │
│  │  ┌────────────┐  ┌──────────┐  ┌────────┐  ┌────┐  │   │
│  │  │ Tavily MCP  │  │ Shopify  │  │  GA4   │  │... │  │   │
│  │  │ @tavily/mcp │  │  MCP     │  │  MCP   │  │    │  │   │
│  │  │ search data │  │ (future) │  │(future)│  │    │  │   │
│  │  └────────────┘  └──────────┘  └────────┘  └────┘  │   │
│  └──────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────┘
            │                                │
            │  user data                     │  Tavily MCP (search)
            ▼                                ▼
┌──────────────────────┐  ┌──────────────────────────────┐
│   User (product      │  │  Tavily Search API           │
│   title, desc, etc)  │  │  (via Tavily's own MCP tool) │
└──────────────────────┘  └──────────────────────────────┘

Layer	Technology	Role
Transport	MCP stdio (JSON-RPC)	Communication between AI agent and server
Validation	Zod	Runtime input parsing and type safety
Tools	TypeScript async functions	Business logic for fashion SEO and trend analysis
Search (external)	Tavily MCP (`@tavily/mcp`)	Real-time web search — separate server, same agent
Integrations	Shopify, GSC, GA4 (future)	Optional future data sources via external MCP

Runtime: Node.js >= 18
Dependencies: @modelcontextprotocol/sdk, zod
No database. No cloud. No framework.

Development

npm run dev          # Dev mode with hot reload
npm run typecheck    # Type checking
npm run build        # Production build
npm run start        # Run the server

Add a new tool

Create src/tools/<name>.ts
Define Zod input schema
Implement execute<Name> function
Export tool object with name, description, inputSchema, handler
Register in src/tools/index.ts

See docs/ARCHITECTURE.md for details.

Project Structure

src/                           # MCP server (Node/TypeScript)
├── index.ts                   # Server entry point + tool registration
├── types/fashion.ts           # Type definitions
├── tools/
│   ├── index.ts               # Tool registry
│   ├── product-seo-audit.ts   # SEO audit tool
│   └── fashion-trend-analysis.ts  # Trend analysis tool
└── integrations/
    └── shopify.ts             # Shopify API client

fashion-mcp-skills/            # Skill pack (12 playbook markdowns)
├── skills/                    # 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
├── MCP-INTEGRATION-GUIDE.md   # External MCP integration guide
└── MARKETPLACE_DEPLOYMENT.md  # Publishing guide

Marketplace

Published on:

Smithery — install with one click
Glama — MCP server discovery
Pulse — MCP directory

See docs/MARKETPLACE_DEPLOYMENT.md for deployment instructions.

Resources

MCP Protocol — Model Context Protocol specification
MCP Servers Directory — Community MCP server catalog
fashion-mcp-skills — Open-source skill pack for fashion retail
MCP Integration Guide — How to connect external MCP servers

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