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Shopops MCP

AI e-commerce operations.

Connector by enzoemir1 · GitHub account verified by the MCP Registry

IndexedMITLocal (stdio)★ 1
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Install

Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/enzoemir1-shopops-mcp

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

AI e-commerce operations. Inventory, pricing, segmentation, and analytics.

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Permissions

What this server can do and what it asks for, from its tool list and install config. Checked Oct 25, 2026.

Tools
10 read · 2 write · 0 delete
Secrets
None asked for
Install scripts
Not scanned yet
Runs as
Node.js package

Tools by what they do 12

Read from the source code; the running server may list different tools.

Read 10 · Look things up without changing anything.

  • customers_churn
  • customers_segment
  • inventory_forecast
  • inventory_status
  • order_anomalies
  • pricing_analyze
  • pricing_optimize
  • product_performance
  • report_daily
  • report_weekly

Write 2 · Create, change, send or run things.

  • store_connect
  • store_demo_seed

Tools (12)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

  • customers_churnChurn Riskread-only

    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.

  • customers_segmentCustomer Segmentationread-only

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

  • inventory_forecastInventory Forecastread-only

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

  • inventory_statusInventory Statusread-only

    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.

  • order_anomaliesOrder Anomaly Detectionread-only

    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.

  • pricing_analyzePricing Analysisread-only

    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.

  • pricing_optimizePricing Optimizationread-only

    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.

  • product_performanceProduct Performance (ABC Analysis)read-only

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

  • report_dailyDaily Reportread-only

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

  • report_weeklyWeekly Reportread-only

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

  • store_connectConnect Store

    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.

  • store_demo_seedSeed Demo Store

    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.

Signals

Stars
1
Forks
1
Open issues
0
Last commit
Sep 13, 2026 (1 month ago)
Last release
Apr 25, 2026 (6 months ago)

From GitHub (fetched Oct 24, 2026).

Details

Registry name
io.github.enzoemir1/shopops-mcp
Version
1.2.2
Auth
No auth
Packages
npm shopops-mcp-server
License
MIT
First seen
Oct 24, 2026
Last checked
Oct 25, 2026

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Shopops MCP. Agent Mart. https://agentmart.dev/connectors/enzoemir1-shopops-mcp (last updated 2026-10-25)