Create a job
Send a POST request to /api/v1/jobs with marketplace set to amazon. Choose input_type based on the source: search_url for Amazon search pages, category_url for category pages, product_url_list for direct product URLs, or asin_list for bulk ASIN enrichment.
Use listing_only when you need discovery rows and ranking positions. Use full_product when you need product page fields such as brand, bullets, images, product details, stock text, shipping information, variants and review signals.
Poll progress
The job status endpoint exposes status, delivered record counts, charged units, speed and export readiness. This lets a script wait for completion without guessing when Amazon data is ready.
The dashboard shows the same lifecycle for non-technical users. That makes ParseShelf easier to roll out because product researchers and developers do not need separate tools.
Download exports
Use the download endpoint when status is succeeded. JSONL is line-oriented for pipelines, CSV is portable for BI tools, XLSX is easiest for spreadsheet users and Markdown helps produce quick internal reports.
All export formats come from the same normalized rows, so a team can inspect a spreadsheet and ship a JSONL pipeline without reconciling two schemas.
Evaluation and rollout notes
A useful Amazon data page should help a buyer decide what to do next, not only define a keyword. When evaluating this workflow, start from the business question: discover products, enrich ASINs, monitor competitors, audit a catalog, prepare a spreadsheet or feed an internal data pipeline. The right ParseShelf mode and export format depend on that question.
For early research, keep the first run small and easy to inspect. Use one keyword, one category, one competitor set or one ASIN list, then compare delivered rows against the fields your team actually needs. Price, currency, rating, review count, stock status, source URL and product URL are usually the minimum useful fields. Product details, bullets, variants, images and seller signals become important when the workflow moves from discovery to enrichment.
For production, treat every Amazon job as an auditable dataset. Store the job ID, input source, mode, run date and export format next to the downstream report. This makes it possible for developers and operators to debug the same result from the dashboard, the API status endpoint and the downloaded files.
When a page links to docs, examples, samples and pricing, the visitor can move from research to implementation without guessing which product surface to use. That path is important for both conversion and search intent satisfaction.
This is also how the page supports organic search quality. The content is tied to a concrete product workflow, includes examples, links to related resources and exposes the same schema that users see after signup. That creates a stronger page than a generic scraper keyword page with no data shape, no implementation detail and no clear next action.