Amazon Product Search

This skill guides you on how to perform Amazon storefront search simulations, helping Amazon sellers retrieve real-time search result data including product rankings, prices, ratings, and more.

Core Concepts

This tool simulates a real user searching on Amazon's storefront. It returns live search result page (SERP) data: product listings with their positions, prices, ratings, review counts, brands, delivery info, sponsored flags, and more. This is real-time data directly from the Amazon frontend, not historical analytics.

Key distinction from ABA data: ABA data is aggregated historical search term analytics. This tool returns the actual product listings a user would see when searching a keyword on Amazon right now.

Keyword language: Keywords should be in the language of the target marketplace. For example, use English keywords for amazon.com, German keywords for amazon.de, Japanese keywords for amazon.co.jp, etc.

Parameters

Supported Marketplaces

Default marketplace is amazon.com. Use amazon.com when the user doesn't specify a marketplace.

Sort Options

Usage

• API Endpoint: POST /amazon/search (see references/api.md for full parameters/response/error codes)
• Python Script: python scripts/amazon_search.py '<JSON params>' [--inline]
• Cost Constraint: This tool consumes credits. The same session+parameter combination is called only once by default; the script has a 24h local cache. Do not auto-retry with different keywords, pages, or zip codes on failure/empty results. Ask the user before additional queries that will incur cost.

Output Strategy (default script behavior):

• Always write the full response to <cwd>/nexscope/<YYYY-MM-DD>/<session>/data/nexscope-amazon-search-<timestamp>.json (<cwd> is the current working directory; <session> is from SESSION_ID env var, auto-grouped by user task; do not write to /tmp, error if current directory is not writable)
• Response ≤ 8 KB: print full JSON to stdout after saving
• Response > 8 KB: stdout prints only a summary (top-level fields, counts like total/costToken, length of largest list fields + first 3 samples)
• Add --inline to force full stdout output (still saves to disk)

Data Reading Tip: Check the summary first. Use jq or ConvertFrom-Json to extract specific fields from the saved JSON file, avoiding loading the full payload into context.

Authentication and Credits

If authentication fails or credits are depleted:

• Ensure NEXSCOPEAPIKEY is set as an environment variable.
• For 401/402 errors or insufficient balance messages, direct the user to https://www.nexscope.ai/help/skills-external-access?co-from=skillNS to top up credits.

How to Build Queries

Construct the request parameters based on the user's intent:

1. Determine the marketplace: Map the user's target country to the correct amazonDomain value
2. Set the keyword: Translate the search term into the target marketplace's language
3. Choose sort order: If the user wants results sorted by price, reviews, or newness, set the sort parameter
4. Pagination: Use the page parameter to fetch additional result pages if needed
5. Category scope: If the user wants to search within a specific category, provide the node parameter
6. Delivery simulation: Use deliveryZip to see location-specific availability and delivery info

Usage Examples

1. Basic keyword search on US marketplace

{"keyword": "wireless earbuds", "amazonDomain": "amazon.com"}

2. Search on German marketplace with German keyword

{"keyword": "kabellose Kopfhoerer", "amazonDomain": "amazon.de", "language": "de_DE"}

3. Search sorted by price (low to high)

{"keyword": "phone case", "amazonDomain": "amazon.com", "sort": "price-asc-rank"}

4. Search for best sellers in a category

{"keyword": "yoga mat", "amazonDomain": "amazon.com", "sort": "exact-aware-popularity-rank"}

5. Search for newest arrivals on Japan marketplace

{"keyword": "USB charger", "amazonDomain": "amazon.co.jp", "language": "ja_JP", "sort": "date-desc-rank"}

6. Multi-page search to analyze deeper results

{"keyword": "laptop stand", "amazonDomain": "amazon.com", "page": 2}

7. Mobile device search simulation

{"keyword": "running shoes", "amazonDomain": "amazon.com", "device": "mobile"}

8. Category-scoped search with delivery zip

{"keyword": "office chair", "amazonDomain": "amazon.com", "deliveryZip": "10001"}

Display Rules

1. Present data clearly: Show search results in well-structured tables with key fields: position, ASIN, title, price, rating, review count, brand
2. Highlight sponsored products: Clearly mark which results are sponsored ads vs organic listings
3. Price formatting: Display prices with the correct currency symbol for the marketplace
4. Position context: Remind users that position reflects the actual ranking on the search result page
5. Pagination notice: When results span multiple pages, inform the user how many total results were found and suggest fetching additional pages if needed
6. Error handling: When a query fails, explain the reason based on the error response and suggest adjusting parameters
7. Image links: If image URLs are available, mention them but do not attempt to render them inline unless the user requests it

Important Limitations

• Real-time only: This tool returns live search results, not historical data. For historical search term analytics, use ABA data instead
• Rate awareness: Each call simulates a real search request; avoid excessive rapid-fire calls
• ~20 results per page: Each page returns approximately 20 product listings
• Keyword language matters: Results quality depends on using the correct language for the target marketplace

User Expression & Scenario Quick Reference

Applicable -- Real-time Amazon search result queries:

Not applicable -- Needs beyond real-time search results:

• Historical search term volume or ranking trends (use ABA data)
• Advertising campaign management or bid optimization
• Product review analysis or sentiment analysis
• Sales estimation or revenue analytics
• Listing optimization or copywriting suggestions
• Inventory or supply chain data

Boundary judgment: When users say "product research" or "competitor analysis", if it boils down to seeing what currently appears on Amazon search results for a keyword (product positions, prices, ratings), then this skill applies. If they want historical trends, search volume data, or aggregated analytics, ABA data is more appropriate.

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