# Reading any marketplace product page from a Chrome extension: JSON-LD first, selectors second

> Source: <https://dev.to/neo_xia_3f4c019330af5fb9d/reading-any-marketplace-product-page-from-a-chrome-extension-json-ld-first-selectors-second-c2i>
> Published: 2026-10-01 19:35:47+00:00

If you have ever tried to build a tool that "reads" a product page — an Amazon listing, a Shopify storefront, an eBay item, an Etsy shop page — you already know the problem: every marketplace renders the same conceptual data (title, bullets, description, images, price) in completely different DOM shapes.

We ran into this while building [AI Product Page Optimization](https://aiproductpageoptimization.com), a Chrome extension that reads the product page you already have open and returns marketplace-ready title, description, and image suggestions in about 30 seconds. This post is the technical breakdown of how the reading step works. (This article is disclosed as AI-assisted; the extraction design and constraints below are from our actual implementation.)

Most marketplaces embed structured data in the page. Before touching a single DOM selector, we look for `application/ld+json` blocks and parse them for `Product`-shaped objects:

``` js
const ldjson = Array.from(
  document.querySelectorAll('script[type="application/ld+json"]')
).map((s) => {
  try { return JSON.parse(s.textContent); } catch { return null; }
}).filter(Boolean);
```

Why JSON-LD first:

`Product` schema block rarely moves.`name`, images are an `image` array, and offers carry price/currency regardless of which marketplace you are on.
But JSON-LD is never the whole story. Marketplaces routinely put *richer or more current* data in the DOM than in their structured data: bullet points that are not in any schema field, variant-specific copy, A/B-tested titles.

After JSON-LD, we apply platform-specific DOM selectors to fill the gaps — and the key word is *scoped*: we detect the marketplace from the URL first, then load only that platform's selector set.

The four surfaces we read today:

Reading the page is half the job. The other half is knowing what each marketplace will accept when you write the optimized copy back:

One more detail that surprised us: output language follows the detected page language. Cross-border catalogs mix English with other locales, and returning English suggestions for a Japanese listing page is worse than useless — it breaks the seller's workflow.

Two things worth knowing if you build this on Manifest V3:

We deliberately never auto-paste anything. Extracted data and generated suggestions land on a web dashboard with a before/after comparison, history, and optional share links — the seller copies and publishes only what they approve. Optimization is credit-based (text 1 credit, AI-enhanced image 2, keeping an original image is free; a typical product page uses about 5 credits, and new accounts start with 5 free credits).

If you are building anything that reads product pages — price trackers, feed tools, listing optimizers — the pattern is the same: structured data first, scoped selectors second, platform constraints third, and a human review step before anything goes back to the marketplace.

Try the extension: [aiproductpageoptimization.com](https://aiproductpageoptimization.com)