Inspect the normalized Amazon product data shape used by ParseShelf exports.
{
"schema_version": "1.0",
"status": "succeeded",
"outcome": "full",
"has_failures": false,
"marketplace": "amazon",
"input_type": "product_url_list",
"mode": "full_product",
"delivered_records": 1,
"records_delivered": 1,
"records_failed": 0,
"charged_units": 5,
"sample": {
"asin": "B0060OUV5Y",
"title": "La Roche-Posay Cicaplast Balm B5 Cream",
"brand": "La Roche-Posay",
"price": 18.99,
"currency": "USD",
"stock_status": "in_stock",
"stock_text": "In Stock",
"product_url": "https://www.amazon.com/dp/B0060OUV5Y",
"rating": 4.7,
"reviews_count": 20743
},
"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"
}
}
# Amazon product sample | ASIN | Product | Price (USD) | | --- | --- | --- | | B0060OUV5Y | La Roche-Posay Cicaplast Balm B5 Cream | 18.99 | Illustrative row. Run a live preview for current data.
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}'
Open a screenshot to inspect the actual path from input to output and observed intelligence.
The sample shows the v1 normalized row shape: asin, title, brand, price, currency, rating, reviews_count, stock_status, stock_text, category_path and product_url. It intentionally omits deprecated aliases so the example is safe to copy into a new integration.
The fields are intentionally practical. They represent the columns most ecommerce teams need for product research, competitor tracking, catalog QA and pricing reports.
JSONL is best for developers because each line is an independent product record. CSV is best for analysts because it opens cleanly in spreadsheets and BI tools.
A real completed job can include more fields when Amazon renders them, including images, bullets, product_details, variants, seller signals, review summaries and source positions.
Before buying an Amazon data tool, compare the sample schema with your downstream needs. If your team needs rank tracking, make sure source_url and position exist. If your team needs catalog enrichment, prioritize product detail fields.
Use the benchmark guide to turn that schema review into a repeatable vendor evaluation.
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.