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pangolinfo

PangolinFo Amazon Ad Tracker & Review Intelligence

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by pangolinfo

get_amazon_reviews

Fetch paginated Amazon reviews for any ASIN. Filter by star rating, sort order, or media type to analyze customer sentiment and uncover pain points.

Instructions

[Amazon 评论批量抓取] 翻页拉某 ASIN 的真实买家评论。可按星级/排序/媒体类型过滤。 Use when: 用户说"看一下 X 的差评""挖痛点""分析竞品评论""做 VOC""为 Listing 找用户原声";或新品立项前差评扫描;或 listing 优化要找改进点。 Don't use: 只看 PDP 自带的几条评论摘要(用 get_amazon_product,里面已含 5-10 条 reviews 和 aiReviewsSummary,对快速判断已经够);做关键词搜索(用 search_amazon)。 Returns: data.json[0].data.results[{ reviewId, date, country, star, title, content, author, authorId, authorLink, imgs[], videos, purchased, vineVoice, helpful, attributes }] — 1 页约 10 条评论。 Pair with: ↑ asin 常来自 search_amazon / get_amazon_product / list_bestsellers;↓ 评论文本可直接给 LLM 做痛点聚类、关键词提取。 Cost: 10 积点/页(贵)。建议先 pageCount=1 探一下,确认有数据再 pageCount=3~5 扩量。filterByStar='critical' 优先(差评信号密度最高)。 Tips: filterByStar 取值 = all_stars / five_star ... one_star / positive / critical;sortBy = recent (默认) | helpful;mediaType = all_contents (默认) | media_reviews_only (带图带视频,真实度更高)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asinYesAmazon ASIN(10 位大写字母+数字)。Examples: 'B09B8V1LZ3' / 'B0CRMZHDG8'。
siteNoAmazon 站点。默认 amz_us。amz_us
pageCountNo拉取的评论页数(1 页约 10 条评论)。**每页扣 10 积点**,请按需控制。默认 1 页。
filterByStarNo按星级筛选。VOC 痛点挖掘建议传 'critical'(1-3 星差评),找改进点;正面卖点提取传 'positive'。all_stars
sortByNo排序:'recent' 按时间倒序(看最新口碑),'helpful' 按帮助票数(看影响力大的评论)。recent
mediaTypeNo评论类型:'all_contents' 全部评论,'media_reviews_only' 仅含图片/视频的评论(更真实可信)。all_contents
zipcodeNo邮编,必须匹配 site 站点所在国家(amz_us → 美国邮编,amz_jp → 日本邮编 …)。可选;不传时后端会从对应国家邮编池随机挑一个。跨国邮编(如 amz_us + 日本邮编)会被后端拒绝。Examples: 10001 (NY) / 90001 (LA) / 100-0001 (Tokyo).
Behavior5/5

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

No annotations provided, but description fully covers behavior: cost per page, pagination, data structure returned, and practical tips like starting with 1 page to test before scaling.

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, but somewhat verbose. Could be slightly more concise while retaining all important information.

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?

Given 7 parameters, no output schema, and no annotations, the description covers all aspects: return format, cost, usage guidelines, and parameter details. Very comprehensive.

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%, yet description adds significant value: explains filterByStar for pain points, pageCount cost warning, zipcode validation, and sortBy/mediaType semantics.

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?

Description clearly states it's for batch scraping real buyer reviews for an ASIN, with filtering options. Distinguishes from siblings like get_amazon_product and search_amazon.

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 (e.g., analyzing negative reviews, VOC) and when-not-to-use (quick PDP review, keyword search). Also suggests pairing with other tools and provides cost-aware recommendations.

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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