MCP server: One MCP server.

Brand Lookalike Watch

@yzf75011-uiMCP server · External

Phishing/typosquat domains imitating a brand, from CT logs. Weekly email: $2.99/mo on Gumroad.

Freeno charge per call

One address for any MCP client. Your key stays in your Odel vault.

What it does

  1. Reads
  2. Writes
  3. High impact

At most: only reads or looks up information.

What it costs

Free

Odel charges nothing per call. The publisher may charge for its own service.

What it asks for at sign-in

Access toReadChange
Full api accessNot stated: full_api_access

From the sign-in at console-backend.apify.com, as the endpoint publishes it. You approve it there.

Tools

None listed · checked 8 Oct 2026

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Try it in the Inspector
README4 min read

New Shopify Stores — MCP tool for AI agents

Give your agent a live feed of new Shopify stores, including pre-launch ones (still behind their password page), plus a Shopify checker for any list of domains. One flat JSON object per store, pay per result, no personal data.

The tool runs on Apify and is served by the official Apify MCP server, so any MCP client (Claude Desktop, Claude Code, Cursor, VS Code, Windsurf, n8n, LangChain, CrewAI) can use it.

Want the full raw database directly? Download the full snapshot (4,000+ stores) for $4.99 — one CSV, no subscription, no agent needed.

Also in this repo: Brand Lookalike Watch — phishing and typosquatting domains imitating a brand, from the same CT logs, as an MCP tool.

Connect in one paste

Remote (recommended, sign in with Apify in the browser): add this to your client's MCP config

json
{
  "mcpServers": {
    "new-shopify-stores": {
      "url": "https://mcp.apify.com/?tools=prelaunch-radar/new-shopify-stores-pre-launch-radar"
    }
  }
}

Local (stdio, with an Apify API token): see clients/local-stdio.json.

Claude Code: claude mcp add --transport http new-shopify-stores "https://mcp.apify.com/?tools=prelaunch-radar/new-shopify-stores-pre-launch-radar"

What the agent can ask

  • "List today's new Shopify stores in the US that are still pre-launch." → {"mode": "feed", "sinceDays": 1, "status": "pre-launch", "countries": ["US"]}
  • "Give me only the stores added since my last call." → {"mode": "feed", "cursor": "<nextCursor from the previous run's OUTPUT>"} (each store is billed once; start with "cursor": "latest", which is free)
  • "Which of these domains run on Shopify, and when were they registered?" → {"mode": "enrich", "domains": ["example.com"]}
  • maxItems caps billed results, so spend is bounded before the call.

Fields: store_domain, store_name, status (live | pre-launch), niche, country, region, currency, products, collections, ships_to, product_types, sample_titles, domain_registered (RDAP), first_detected, status_checked_at and status_since (dated status checks), cursor.

Automate it: new stores into Clay, n8n, Make or your CRM

No server to run. Import integrations/n8n/new-shopify-stores-daily.json into n8n (Workflows → Import from file):

  1. Every morning at 8 triggers the workflow.
  2. Set your settings holds everything you change: webhookUrl, startCursor (0 = the last 30 days, latest = only stores added from now on), status (any, pre-launch or live), countries (e.g. US, GB, empty = all) and maxItems (100), which caps what one run can bill.
  3. Load cursor reads the position saved by the last successful run.
  4. Get new Shopify stores (Apify) calls the Actor with that cursor, so it returns and bills only the stores added since. Add a Header Auth credential: name Authorization, value Bearer <your Apify API token>.
  5. Keep store fields keeps domain, name, status and its check date, niche, country, currency, products, dates and cursor.
  6. Send each store to your webhook posts one JSON object per store to any URL: a Clay table webhook, a Make custom webhook, Slack, HubSpot, your own endpoint.
  7. Save cursor moves the position forward once the webhook calls succeed. n8n keeps it only in active (scheduled) runs, so a manual test run starts from startCursor again.

Lead scoring version: ranked stores into Google Sheets and Slack

integrations/n8n/new-shopify-stores-lead-scoring.json goes further: it uses the same cursor, so each store arrives and is billed once, scores each new store from 0 to 10 (pre-launch, your target niches and countries, small catalog, ships abroad, young domain) with the reasons written out, logs every store in Google Sheets (updated by domain), sends one Slack alert per hot store and one digest for the warm ones. Sample data is pinned, so a first test run needs no Apify token.

Also in the official n8n template gallery, ready to import in one click: n8n.io/workflows/19966. The gallery copy follows n8n's review schedule and may still use the older sinceDays setting; the files in this repository are the latest.

A niches filter can be added to the Apify request body. The feed holds business-level public data only; any contact enrichment you add downstream is your own processing, under your own compliance.

How it works

Public Certificate Transparency logs (RFC 6962) → public DNS records pointing to Shopify → official RDAP registry → each store's public /meta.json and /products.json.

This dataset is provided for market research, trend analysis, and competitive intelligence purposes only, on an "as-is" and "as-available" basis without warranties of any kind. Records originate exclusively from publicly accessible sources (Certificate Transparency logs, public DNS and RDAP registries, and stores' public storefront metadata); no personally identifiable information (PII) is collected or sold. Independently developed, not affiliated with, authorized, or endorsed by Shopify Inc. or Apify. By accessing this data, you agree that the provider shall not be liable for any direct or indirect damages resulting from its use; in any event, liability is limited to the purchase price.

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