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pangolinfo

Amazon All-in-One Scrape MCP

Official
by pangolinfo
OverviewSchemaRelated ServersScoreDiscussions(1)
TypeScript
Hybrid

get_amazon_reviews

Fetch real Amazon reviews for any ASIN, filterable by star rating, sort order, and media type. Ideal for extracting customer pain points and analyzing competitor feedback.

Instructions

[Amazon review batch scrape] Page-fetch real buyer reviews for an ASIN. Filterable by star / sort / media type. Use when: user says "look at X's negative reviews" / "mine pain points" / "analyse competitor reviews" / "do VOC" / "find user complaints for Listing copy"; or pre-launch critical-review scan; or finding improvement points for listing optimization. Don't use: when the few reviews already in the PDP would suffice (get_amazon_product carries 5-10 reviews + aiReviewsSummary — enough for a quick read); for keyword search (use search_amazon). Returns: data.json[0].data.results[{ reviewId, date, country, star, title, content, author, authorId, authorLink, imgs[], videos, purchased, vineVoice, helpful, attributes }] — ~10 reviews per page. Pair with: ↑ asin typically from search_amazon / get_amazon_product / list_bestsellers; ↓ review text can be fed directly to an LLM for pain-point clustering and keyword extraction. Cost: 10 points per page (expensive). Start with pageCount=1 to confirm data, scale to 3-5 only when needed. Prefer filterByStar='critical' — highest signal density. Tips: filterByStar = all_stars / five_star ... one_star / positive / critical; sortBy = recent (default) | helpful; mediaType = all_contents (default) | media_reviews_only (with photos/videos, higher credibility).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asinYesAmazon ASIN (10-char uppercase alphanumeric). Examples: 'B09B8V1LZ3' / 'B0CRMZHDG8'.
siteNoAmazon marketplace. Defaults to amz_us.amz_us
pageCountNoNumber of review pages to fetch (~10 reviews per page). **Costs 10 points per page** — control accordingly. Defaults to 1.
filterByStarNoFilter by star rating. For VOC pain-point mining, pass 'critical' (1-3 star reviews) to surface defects; for positive-aspect extraction, pass 'positive'.all_stars
sortByNoSort order: 'recent' (newest first — track current sentiment) or 'helpful' (most-upvoted first — highest impact reviews).recent
mediaTypeNoReview type: 'all_contents' for all, 'media_reviews_only' for reviews with photos/videos only (higher credibility).all_contents
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).

Tool Definition Quality

A4.8/5.0
Behavior5/5

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

No annotations provided, but description fully discloses expensive cost (10 points per page), recommends starting with pageCount=1, and provides strategic tips on filters. Also explains return structure and pairing with other tools for downstream tasks.

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-structured with sections (use when, don't use, returns, cost, tips) and front-loaded with purpose. Though lengthy, each sentence adds value; slight reduction possible without losing clarity.

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?

No output schema, but description provides detailed return structure (fields like reviewId, date, star, content) and explains pairing with other tools. Covers all aspects for a complex 7-param tool with enums and strategic advice.

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

Parameters4/5

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

Schema coverage is 100%, baseline 3. Description adds significant value by explaining the purpose of each filter (e.g., 'critical' for pain-point mining), giving usage examples, and providing cost/strategy tips beyond schema details.

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?

Clearly states batch scraping of real buyer reviews for an ASIN with filtering options. Uses specific verb 'page-fetch' and resource 'reviews', distinguishing from get_amazon_product which carries only 5-10 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?

Explicitly lists use cases (e.g., 'look at X's negative reviews', 'mine pain points') and when not to use (when PDP reviews suffice or for keyword search), with alternative tools named (get_amazon_product, search_amazon).

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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