Use these docs to create Amazon jobs, choose the right input type, poll progress and download structured exports from ParseShelf.
{
"status": "succeeded",
"marketplace": "amazon",
"input_type": "product_url_list",
"mode": "full_product",
"delivered_records": 1,
"charged_units": 5,
"sample": {
"product_id": "B0060OUV5Y",
"title": "La Roche-Posay Cicaplast Balm B5 Cream",
"brand": "La Roche-Posay",
"price": 18.99,
"currency": "USD",
"ASIN": "available when Amazon renders this field",
"rating": 4.7,
"reviews_count": 20743,
"stock": "In Stock",
"images": [
"https://m.media-amazon.com/images/I/713Ca5aXpyL._SL1500_.jpg"
],
"category_path": [
"Beauty & Personal Care",
"Skin Care",
"Face",
"Creams"
],
"product_url": "https://www.amazon.com/dp/B0060OUV5Y"
},
"exports": {
"jsonl": "/api/v1/jobs/{job_id}/download/jsonl",
"csv": "/api/v1/jobs/{job_id}/download/csv",
"xlsx": "/api/v1/jobs/{job_id}/download/xlsx",
"md": "/api/v1/jobs/{job_id}/download/md"
}
}
curl -X POST https://parseshelf.com/api/v1/jobs \
-H 'Authorization: Bearer $PARSESHELF_KEY' \
-H 'Content-Type: application/json' \
-d '{"marketplace":"amazon","input_type":"product_url_list","input_value":"https://www.amazon.com/dp/B0060OUV5Y","mode":"full_product","target_count":20}'
A ParseShelf job starts with a marketplace, input type, input value, mode and target count. The same contract works for Amazon search URLs, category URLs, product URLs and ASIN lists, so a developer can switch sources without rewriting downstream export logic.
The dashboard and API share the same job lifecycle. Operators can create and inspect jobs in the web product while engineers automate recurring runs with API keys.
Completed jobs expose normalized rows with fields such as product_id, asin, title, brand, price, currency, rating, reviews_count, stock_status, category_path, images and product_url. The exact fields depend on what Amazon renders for a page, but exports keep a predictable schema.
Each export format is generated from the same normalized rows. JSONL is best for pipelines, CSV and XLSX are best for analysts, and Markdown is useful for internal reports.
Start with the API overview if you need the complete endpoint map. Use the Python or JavaScript quickstart when you are wiring ParseShelf into a backend. Use the Google Sheets and Postman guides when the first user is an operator or analyst.
For SEO and research workflows, pair these docs with the public sample dataset and benchmark page so buyers can evaluate the data shape before signing up.
Start with one small input and compare the delivered fields with the report, catalog or pipeline you actually need.
Keep the job ID, source URL, mode and run date with downstream exports so operators and developers can audit the same result.