Fashion E-Commerce Intelligence● live

# Fashion E-Commerce Intelligence MCP Server Empower your AI agents, copilots, and assistants with vertical domain-specific fashion intelligence. Built by [alexgenovese.com](https://alexgenovese.com), this Model Context Protocol (MCP) server provides automated **SEO auditing, category trend research, DTC demand forecasting, CRM segment enrichment, and campaign creative generation**—designed natively for Cursor, Claude Desktop, Claude Code, and any MCP-compatible environment. --- ## ⚡ What It Does | Capability | Without this server | With Fashion MCP | |---|---|---| | **SEO Auditing** | Manual checklists, generic advice | Automated **0-100 score** across 5 dimensions with fashion-specific validation (fit, material, color, season, schema, OG, Twitter Cards). | | **Trend Research** | Generic search queries | Deep category-level trend intelligence (trending keywords, colors, silhouettes, price tiers, market fit, confidence). | | **Demand Forecasting** | Spreadsheets & gut feel | Explainable weighted-rule forecast model: `baseline × trend × media × retention × inventory × seasonality` with backtesting. | | **CRM Segment Enrichment** | Static email lists | Customer segments enriched with trending colors/silhouettes/keywords, audience clusters, and ready-to-send targeted messaging. | | **Campaign Creative** | Standard copywriters per channel | Platform-specific campaign themes, hooks, value propositions, and ready-to-use creative briefs grounded in real-time trend data. | --- ## 🔧 Exposed Tools - `product_seo_audit`: Audit any fashion product listing for SEO best practices, structured schema, meta tags, and fashion relevance (fit, size, material). - `fashion_trend_analysis`: Analyze fashion trend data mapped from search engine and social media queries. *(Receives search data input seamlessly, no API key required)*. - `fashion_category_demand_outlook`: Get a rapid, light demand pulse check for a specific product category in a target market and season. - `fashion_dtc_forecast_analysis`: Generate complete DTC demand forecasts with scenario planning (base/upside/downside) and stock-out/overstock risk analysis. - `fashion_customer_trend_enrichment`: Map trending keywords, colors, and silhouettes onto existing CRM customer segments for targeted email/SMS campaigns. - `fashion_campaign_theme_recommendation`: Recommend conversion-optimized ad campaign themes, hooks, and creative briefs for Meta, Google, TikTok, or Email. --- ## 🔐 Privacy & Technical Features - **No API Keys Required:** All analysis, SEO audits, demand forecasts, and CRM enrichment tools run locally with the data you supply. Upstream search data for `fashion_trend_analysis` uses your existing Tavily MCP server. - **Explainable Outputs:** Every tool returns explicit `assumptions[]`, `dataGaps[]`, and `confidence` scores, so your agent always understands the reasoning behind recommendations. - **Zero-Config Local Cache:** Uses a local SQLite persistence layer (`~/.fashion-mcp/store.db`) to cache snapshots, feature stores, and forecast actuals for backtesting. ## 💻 Local Installation To install this stdio-based server locally via Smithery: ```bash npx smithery install alexgenovese/ecommerce-fashion-market-analysis ```