Zalando
www.zalando.comLast scanned Jun 20, 2026, 6:21 AM
Verdict
Open to AI agents
ARC Score v1.0
63/100Mixed
Agent access50/50
Structured data3/25
Protocol files0/15
Scan stability10/10
Agent access
| Agent | Status | Source |
|---|---|---|
| GPTBot | Allowed | robots.txt: no rule (default allow) |
| ChatGPT-User | Allowed | robots.txt: no rule (default allow) |
| ClaudeBot | Allowed | robots.txt: no rule (default allow) |
| Claude-Web | Allowed | robots.txt: no rule (default allow) |
| PerplexityBot | Allowed | robots.txt: no rule (default allow) |
| Google-Extended | Allowed | robots.txt: no rule (default allow) |
| Amazonbot | Allowed | robots.txt: no rule (default allow) |
| Bingbot | Allowed | robots.txt: no rule (default allow) |
| CCBot | Allowed | robots.txt: no rule (default allow) |
Infrastructure
PlatformcustomCDNcloudfrontWAFnone
Data signals
- JSON-LDNot detected
- Schema.org ProductNot detected
- Open GraphNo
- Product feedYes
- llms.txtNo
Change history · last 90 days
0 changesNo confirmed changes in the last 90 days — this brand's agent access posture has been stable.
The free public window is 90 days. Multi-year history, watchlists, and change alerts are part of Pro.
Fix it with Claude Code
Each failed check below has a ready-made prompt tailored to this scan's findings. Run it in Claude Code from your site's repository.
✗No JSON-LD structured datashow prompt
My e-commerce site is zalando.com. A scan found no JSON-LD structured data at all, so AI shopping agents can't reliably read our product names, prices, availability, or images. In this repository: 1. Find the product page template/component and add a JSON-LD <script type="application/ld+json"> block with Schema.org Product markup: name, description, image, sku, brand, and an Offer with price, priceCurrency, availability (use schema.org/InStock | OutOfStock), and url. Populate every field from our real product data — no placeholders. 2. Add an Organization JSON-LD block to the base layout (name, url, logo) if missing. 3. If we have category/listing pages, add ItemList markup referencing the product URLs. 4. Show me one fully rendered example of the JSON-LD for a real product, then validate the shape against Google's Rich Results requirements for Product and fix any warnings you can detect statically. Verify after deploy with https://search.google.com/test/rich-results on a product URL.
✗No Open Graph tagsshow prompt
My e-commerce site is zalando.com. A scan found no Open Graph meta tags, so link previews and many AI agents see untitled, imageless pages. In this repository: 1. Add og:title, og:description, og:image, og:url, and og:type to the base layout's <head>, with sensible site-wide defaults. 2. On product pages, override them per product: og:type "product", the product image as og:image (absolute URL, ≥1200×630 where available), and the live price in og:description. 3. Add twitter:card "summary_large_image" alongside. 4. Show me the diff and one rendered <head> for a real product page.
✗No sitemap.xmlshow prompt
My e-commerce site is zalando.com. A scan could not find a sitemap.xml, so crawlers and AI agents have no reliable way to discover our product pages.
In this repository:
1. Generate a sitemap.xml covering the homepage, category pages, and every product page, with <lastmod> from each product's updated-at where available.
2. If the catalog is large, split into a sitemap index with child sitemaps of ≤50,000 URLs.
3. Reference the sitemap from robots.txt ("Sitemap: https://zalando.com/sitemap.xml").
4. Make it regenerate automatically (build step or on-demand route) rather than a one-off static file, and show me the diff.✗No llms.txtshow prompt
My e-commerce site is zalando.com. We don't publish an llms.txt file yet — the emerging convention (llmstxt.org) that gives language models a concise, curated guide to a site. In this repository: 1. Create /llms.txt (served at https://zalando.com/llms.txt as text/plain or text/markdown) following the llms.txt format: - H1 with our brand name, - a one-paragraph blockquote summary of what we sell and who we serve, - sections linking to our most useful pages for an AI agent: bestsellers/category pages, shipping & returns policy, size guides, FAQ/support, and store locator if any. 2. Write the summary from this repository's real content (README, about page, homepage copy) — keep it factual, no marketing superlatives. 3. Keep it under ~200 lines, every link absolute. 4. Show me the full file content and where you wired it to be served. Verify after deploy: curl https://zalando.com/llms.txt
Embed this score
HTML
<a href="https://www.arcreport.ai/brand/zalando"><img src="https://www.arcreport.ai/badge/zalando.svg" alt="ARC Score for Zalando" height="22"></a>
Markdown
[](https://www.arcreport.ai/brand/zalando)
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