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Pangolinfo Amazon Data MCP

get_amazon_product

Retrieve complete Amazon product details for a single ASIN, returning structured data on price, rating, seller, variants, and reviews.

Instructions

[Amazon single-product detail] Scrape the full PDP for one ASIN. Use when: user supplies a specific ASIN ("look at B0XXXXXXXX" / "check this product's price/rating/seller" / "analyse this competitor"); or as a SOP step after candidate ASINs are picked. Don't use: for many products at once (use search_amazon or list_* series for lists); for reviews only (use get_amazon_reviews — cheaper and more focused). Returns (format='json', default): data.json[0].data.results[0] = { asin, title, itemName, itemHighlights, price, star, rating, brand, seller{name,id,link,hasLink}, parentAsin, videos[{type,section,mp4,m3u8,previewMp4,cover,title,duration,author}], shippingFee (buyer shipping fee as a number, e.g. "750"; "0" when free shipping or no info, varies by the zipcode address), delivery{deliveryTime,fastestDelivery}, ratingDistribution[], aiReviewsSummary, bestSellersRankItems, reviews[{date,star,content,helpful,...}], productOverview[], features[], productDescription[], images[], variantDetails[], attributes[], category_id, breadCrumbs, ... } — 30+ fields (variantDetails summary included). videos[].section: product=main media gallery, brand=brand module, related=related videos, customerReview=customer review videos. Use get_amazon_delivery_time when you need the high-return warning, free/paid/fastest delivery breakdown, or handling lead time. Title fields (Amazon split the title into two parts starting 2026-07-27): title=the full raw title string (for rolled-out listings it contains a " | " separator, unsplit); itemName=the title body (the part before " | ", i.e. the product name, ≤75 chars); itemHighlights=the title highlights (the part after " | ", e.g. material/use-case/selling points, ≤125 chars). For legacy (not-yet-rolled-out) listings itemName=the full title and itemHighlights is an empty string. Use itemName for the clean product name, itemHighlights for selling points. Pair with: ↑ asin typically comes from search_amazon / list_bestsellers / filter_niches; ↓ feed the same asin into get_amazon_reviews for more reviews (the PDP carries only ~5-10). Cost: ~1 point/call, ~5s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asinYesAmazon ASIN, 10 letters/digits (case-insensitive — auto-uppercased). Example: 'B0B4NLGCH5'.
siteNoAmazon marketplace. Defaults to 'amz_us' (US).amz_us
formatNoResponse format. Defaults to 'json' — a structured payload (title, price, rating, reviews, seller, etc.) ready for programmatic use. Use 'markdown' if you want the rendered PDP text instead.json
zipcodeNoZIP code that must match the site country (amz_us → US zip, amz_jp → JP zip, ...). Optional; backend picks a random one from the per-country pool when omitted. Cross-country zips (e.g. amz_us + JP zip) are rejected by the backend. Examples: 10001 (NY) / 90001 (LA) / 100-0001 (Tokyo).
clientSourceNo调用来源标记。仅由 Pangolinfo Skill 传 skill;普通 MCP 调用省略即可。

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does well: it discloses cost (~1 point/call, ~5s), the fact that the PDP carries only ~5-10 reviews, and the exact return shape. It stops short of stating auth requirements, rate limits, or failure behavior, which keeps it out of 5 territory.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well front-loaded with labeled sections (purpose, use/don't use, returns, pairing, cost). The dense "Returns" block is long but justified given there is no output schema; only minor redundancy in restating the title split.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 5-parameter read tool with no output schema and no annotations, the description supplies everything an agent needs: return-field enumeration, title/itemName/itemHighlights semantics, video section meanings, cost, and a pointer to get_amazon_delivery_time for delivery nuance.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all five parameters (asin, site, format, zipcode, clientSource) are already documented, including the zipcode cross-country rejection rule. The description adds little about inputs beyond the default format, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Scrape the full PDP for one ASIN") and immediately distinguishes itself from siblings by naming search_amazon, the list_* series, and get_amazon_reviews. An agent can route correctly without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit "Use when" conditions (user supplies a specific ASIN, or as a SOP step after candidate ASINs) and explicit "Don't use" exclusions with named alternatives for both bulk lookups and review-only needs. Pairing notes (upstream/downstream) close the loop.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.