Amazon research workflow

Amazon CSV Sample Generator.
Clean Amazon data.

Preview spreadsheet-ready Amazon rows for operators, analysts and catalog workflows.

Try live demo Create Amazon job View API docs

Terminal

$ curl -H "Authorization:Bearer $PARSESHELF_KEY" -d "q=vitamin c serum&limit=50" https://api.parseshelf.com/api/amazon

ASINPriceRatingImagesStockExports

Keyword intents

Built for common Amazon data workflows.

Amazon URLsASINsLive jobsExportsDashboard

API example

Request Amazon CSV Sample Generator from the dashboard or API.

This example shows the exact shape a developer can use to create a ParseShelf Amazon job. Operators can run the same workflow in the web dashboard, inspect live rows, and download files without writing code.

Use product_url_list when the seed is a product url list. Use full_product when you need product page fields such as brand, bullets, stock text, shipping information, variants, media and review signals. Use listing_only for fast discovery scans before enriching selected ASINs.

Every completed job exposes the same normalized contract across API responses, JSONL exports, CSV files, Excel workbooks and Markdown reports, so downstream systems do not need a separate parser for each Amazon page type.

curl requestPOST /api/v1/jobs

curl -X POST https://parseshelf.com/api/v1/jobs \
  -H "Authorization: Bearer $PARSESHELF_KEY" \
  -H "Content-Type: application/json" \
  -d '{"marketplace": "amazon", "connector": "amazon_v1", "input_type": "product_url_list", "input_value": "https://www.amazon.com/dp/B0060OUV5Y\nhttps://www.amazon.com/dp/B08H3JPH74", "mode": "full_product", "target_count": 50, "filters": {"currency": "USD", "delivery_profile": "balanced", "auto_repair": true}}'

sample responseJSON

{
  "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",
    "rating": 4.7,
    "reviews_count": 20743,
    "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"
  }
}

Workflow

How teams use Amazon CSV Sample Generator in production.

1Choose the Amazon input. Start from a search URL, category URL, direct product URL or ASIN list. ParseShelf normalizes the input and keeps the original source URL on each row for auditability.
2Pick discovery or enrichment depth. Listing-only jobs return ranked rows quickly. Full-product jobs open product pages and add deeper fields such as product details, image galleries, shipping information, seller signals, variants and review metrics.
3Preview rows while the worker runs. The dashboard shows delivered records, speed, quality signals, fallback rows and export status, so non-technical users can see whether a job is producing useful Amazon data before the final download.
4Export clean files or automate with API keys. Download JSONL, CSV, XLSX and Markdown files, or poll the job endpoint and pull the same artifacts into a warehouse, pricing monitor, catalog QA workflow or internal reporting system.

Data quality

What makes this different from a raw scraper.

Need	ParseShelf behavior	Why it matters
Stable schema	Amazon-specific fields are normalized into predictable keys such as `product_id`, `price`, `rating`, `images` and `product_url`.	Your exports can feed spreadsheets, scripts and dashboards without field-by-field cleanup after every job.
Transparent quality	Fallback listing rows are marked separately from full product rows when Amazon product pages are slow or unavailable.	Teams can decide whether fast discovery data is enough or whether selected ASINs should be repaired and enriched again.
Operator workflow	The web panel handles job creation, progress, row preview, item detail pages and file downloads.	Researchers and ecommerce operators can use the product without maintaining proxies, browsers or scraper scripts.
Developer workflow	API keys create jobs, inspect status and download exports from authenticated endpoints.	Developers can automate recurring Amazon data collection while the rest of the team uses the same data in the dashboard.

Proof from a live parser run

Amazon CSV Sample Generator should be judged by delivered rows, not promises.

ParseShelf is built around auditable Amazon jobs: the dashboard shows live progress, delivered rows, internal misses, speed and export readiness while the worker runs.

In a recent full-product QA run from an Amazon search URL, ParseShelf delivered 10 of 10 target records with 0 failed records, first delivery in about 6 seconds, and clean JSONL/CSV/XLSX/Markdown exports mirrored to object storage.

That run exposed product IDs, titles, prices, ratings, review counts and image galleries for every delivered product. A follow-up normalization improvement now removes Amazon buybox and delivery UI text from category breadcrumbs before export.

Quality signal	Observed result
Target records	10
Delivered records	10
Failed records	0
Product attempts	10 successful first-attempt product fetches
Export formats	JSONL, CSV, XLSX and Markdown
Fields present on all rows	product_id, title, price, currency, rating, reviews_count, images and product_url

CSV preview

Generate a sample Amazon CSV row.

ASIN Title

product_id,title,price,rating,reviews_count,product_url

Next step

Turn this result into a real Amazon data job.

Run Amazon CSV Sample Generator output through ParseShelf

Use the free tool for quick cleanup, then create a ParseShelf job to enrich ASINs, product URLs, search URLs or category pages with prices, ratings, reviews, images, stock and spreadsheet-ready exports.

Create Amazon job Estimate units

Schema

Fields this API returns.

product_id

Normalized `product_id` field in JSONL, CSV, XLSX and Markdown exports.

title

Normalized `title` field in JSONL, CSV, XLSX and Markdown exports.

brand

Normalized `brand` field in JSONL, CSV, XLSX and Markdown exports.

price

Normalized `price` field in JSONL, CSV, XLSX and Markdown exports.

currency

Normalized `currency` field in JSONL, CSV, XLSX and Markdown exports.

rating

Normalized `rating` field in JSONL, CSV, XLSX and Markdown exports.

reviews_count

Normalized `reviews_count` field in JSONL, CSV, XLSX and Markdown exports.

Use cases

Research, monitor and export.

Market research

Use Amazon CSV Sample Generator for market research with live job progress and downloadable files.

Competitor tracking

Use Amazon CSV Sample Generator for competitor tracking with live job progress and downloadable files.

Catalog intelligence

Use Amazon CSV Sample Generator for catalog intelligence with live job progress and downloadable files.

Automated reporting

Use Amazon CSV Sample Generator for automated reporting with live job progress and downloadable files.

More Amazon data workflows.

FAQ

Amazon CSV Sample Generator questions.

Can ParseShelf handle amazon csv sample?

Yes. Create a web job or API job and export structured Amazon rows when parsing completes.

Can I run this without code?

Yes. The dashboard supports job creation, live progress, preview rows and downloads.

Can developers automate it?

Yes. API keys can create jobs, poll progress and download exports.

Amazon CSV Sample Generator.Clean Amazon data.