io.github.enzoemir1/shopops-mcp

Product performance report with ABC analysis. Category A = top 80% revenue, B = next 15%, C = bottom 5%. Includes trends, margins, and daily sales velocity.

Daily operational report: orders, revenue, top products, new vs returning customers, low stock alerts, and anomaly count.

Weekly trend report: revenue/order changes vs previous week, customer segment distribution, trending products, and AI-generated insights.

Manage Shopify or WooCommerce store connections. action="connect" adds a new store and performs an initial sync of products, orders, and customers; action="sync" refreshes cached data for an existing store; action="list" returns all connected stores with their sync counts. Returns a JSON payload with store metadata (id, name, platform, url, counts, last_sync) — credentials are never returned.

Create a realistic demo store populated with 20 products, 40 customers across 6 archetype buckets (champions, loyal, new, at-risk, hibernating, one-off), and 150+ orders spanning the last 6 months. Use this to explore ShopOps without real Shopify or WooCommerce credentials — every tool (inventory_status, customers_segment, order_anomalies, report_weekly, etc.) will return meaningful output on the returned store_id. Safe to call multiple times; each call creates a new demo store with a unique ID. Returns the store_id plus product/customer/order counts.

Snapshot of current stock levels for a connected store. Returns a summary object with total product count, out-of-stock count, low-stock count (≤10 units), plus two arrays: out_of_stock and low_stock — each containing product id, title, sku, quantity, and status. Items are sorted by urgency (lowest quantity first). Read-only and idempotent.

Predict stock depletion dates using moving-average sales velocity. Returns reorder points, safety stock levels, and suggested reorder quantities for each product.

Analyze pricing across products with margin calculation, sales velocity, and rule-based price optimization suggestions. Returns an array where each element contains product_title, current_price, cost, margin_percent, daily_units_sold, revenue_per_day, suggested_price (or null if no change recommended), and suggestion_reason. Pass product_id to scope to a single product, omit for full catalog.

Filtered pricing recommendations — only products where a price change is suggested. Returns a summary with total_suggestions count and an optimizations array (product, current_price, suggested_price, change_percent, reason, daily_revenue), sorted by absolute change_percent (biggest moves first). Use this instead of pricing_analyze when you only want actionable changes.

RFM (Recency, Frequency, Monetary) customer segmentation. Categorizes customers into segments: Champions, Loyal, Potential, At Risk, New, Hibernating, Lost — with actionable recommendations.

Identify customers at risk of churning based on RFM recency + frequency signals. Returns an object with at_risk, hibernating, and lost arrays — each contains customer id, name, email, last_order_date, days_since_last_order, total_spent, total_orders, and a win_back_recommendation string. Use this for targeted re-engagement campaigns.

Statistical anomaly detection on recent orders. Flags high-value orders (>3σ from mean), velocity spikes (customer ordering unusually fast), unusual quantities, off-hours purchases (2am-5am), and new-customer high-value orders. Returns an array of anomalies with order_id, anomaly_type, severity (low/medium/high), reason, and recommended_action. Useful for fraud detection and revenue spike investigation.