Shopify Dropshipping & Store Trust Checker

Give it Shopify store domains (or a dataset of store URLs) and get evidence-based signals per store: a dropshipping likelihood and a store-trust band, each with the exact evidence behind it. It reads only Shopify's public endpoints and the storefront — no keyword search, no paid child actor — so it is fast and cheap on big lists.

⚠️ Signals, not accusations. Every output is a labelled, automated signal from public data. A store can show strong dropshipping signals and still be a perfectly legitimate business. Use this for due-diligence and research, and verify before you act on it. The actor never calls a store "a scam".

📋 What it checks (per store)

Dropshipping signals

• % of product images served from AliExpress / Alibaba CDNs (alicdn) — the strongest public tell.
• Fake-discount markups — median compare-at ÷ price (dropshippers inflate the "was" price).
• Copy-paste product titles — long, keyword-stuffed titles typical of imported listings.
• Brand-new catalogue — most products added in the last 90 days on a tiny store.
• Dropship-associated apps — Vitals, Zendrop, DSers, CJ, AutoDS, Spocket… when detectable.

Trust signals

• Public contact email and social profiles, product reviews app, Shop Pay, catalogue age (oldest product), catalogue size. These combine into a trust band: established / mixed / limited.

📥 Input

Field	What it does
domains	The Shopify stores to check, e.g. ["somestore.com"]. Storefront domains, not admin URLs.
datasetId	Any dataset whose rows contain store URLs. Read-only. Check the output of a store finder or ad-library run.
onlyLikelihood	Keep only stores at medium+ or high dropshipping likelihood.
concurrency	How many stores are checked at once (default 8).
webhookUrl	Optional: checked stores POSTed here as JSON.

📤 Output (one row per store)

An established brand reads clean (real run — allbirds.com):

{
  "storeName": "Allbirds",
  "url": "https://allbirds.com",
  "dropshippingLikelihood": "low",
  "dropshippingScore": 0,
  "dropshippingSignals": ["no notable dropshipping signals in public data"],
  "trustBand": "established",
  "trustScore": 98,
  "trustSignals": ["public contact email", "6 social profile(s)", "on-site product reviews app", "catalogue over a year old (oldest product 2894 days)"],
  "riskFlags": ["none detected"],
  "aliImagePct": 0.0, "markupMedian": 2.0, "oldestProductDaysAgo": 2894,
  "signalsOnly": true,
  "note": "Automated signals from public storefront data, not proof of any wrongdoing. Verify before acting."
}

A high-signal store instead returns, e.g., dropshippingLikelihood: "high" with dropshippingSignals listing the AliExpress image %, the markup multiple, the copy-paste titles and the brand-new catalogue, and trustBand: "limited" with the matching risk flags.

💰 Pricing (pay-per-event)

• Store checked — $0.004 per verified Shopify store. Non-Shopify or unreadable inputs are free. No search bill.

💡 Use cases

• Buyer due-diligence: is this store a real brand or a reshipped-from-AliExpress markup?
• Dropshipping research: profile a list of stores and rank them by dropshipping signals.
• Marketplace / affiliate trust screening: flag stores with limited trust signals before you partner.
• Enrichment: pass the datasetId of any store-finder or ad-library run.

🚀 Ready-made tasks (one click)

• Shopify Dropshipping Checker: paste domains, get dropshipping likelihood + evidence per store.
• Is This Shopify Store Legit? Trust Checker: trust band, contacts, reviews and catalogue age per store.
• Find AliExpress-Sourced Shopify Stores: keep only stores with medium/high dropshipping signals.

🤖 For AI agents (MCP)

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=actors,docs,centorprat/shopify-dropshipping-checker"
    }
  }
}

Claude Code: claude mcp add --transport http apify "https://mcp.apify.com/?tools=actors,docs,centorprat/shopify-dropshipping-checker" --header "Authorization: Bearer <YOUR_APIFY_TOKEN>"

📜 Data & attribution

Reads only publicly available Shopify endpoints and storefronts. No login, no private data. Output is automated signals for research and due-diligence, not a determination of fraud.

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