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pangolinfo

Amazon All-in-One Scrape MCP

Official

get_amazon_product

Retrieve comprehensive product data for one ASIN: price, ratings, seller, reviews, features, and more. Returns 30+ fields in JSON or markdown format.

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, price, star, rating, brand, seller{name,id}, parentAsin, ratingDistribution[], aiReviewsSummary, bestSellersRankItems, reviews[{date,star,content,helpful,...}], productOverview[], features[], productDescription[], images[], variantDetails[], attributes[], category_id, breadCrumbs, ... } — 30+ fields (variantDetails summary included). 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 chars uppercase. Examples: 'B09B8V1LZ3' (Echo Dot 5) / 'B0CRMZHDG8' (Stanley Quencher) / 'B0BDHWDR12' (AirPods Pro 2).
siteNoAmazon marketplace. Defaults to 'amz_us' (US).amz_us
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).
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
Behavior5/5

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

No annotations exist, so the description carries full burden. It discloses cost (~1 point/call, ~5s), that PDP carries only ~5-10 reviews, and details the return structure. This is highly transparent.

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

Conciseness5/5

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

Every sentence is purposeful, structured with clear sections (use/don't use/returns/pair/cost). There is zero waste, and it is front-loaded with the core purpose.

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?

Despite no output schema, the description fully enumerates the return fields. It covers cost, timing, pairing, and limitations. The description is complete for an agent to invoke correctly.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds value with examples (ASIN examples), usage context for zipcode (cross-country rejection), and format explanations (json vs markdown). It also outlines the entire return object.

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?

The description starts with 'Scrape the full PDP for one ASIN', which is a specific verb+resource. It clearly distinguishes from siblings like search_amazon and get_amazon_reviews.

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 when-to-use (specific ASIN, SOP step), when-not-to-use (many products, reviews only), and alternatives (search_amazon, list_*, get_amazon_reviews) are all provided.

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

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