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pangolinfo

PangolinFo Amazon Ad Tracker & Review Intelligence

Official
by pangolinfo

search_amazon

Search Amazon by keyword to retrieve a list of ASINs with details including title, price, rating, and sales. Use results for competitor analysis or further product research.

Instructions

[Amazon SERP 抓取] 用关键词在 Amazon 上跑一次真实搜索,拿回搜索结果首屏 ASIN 列表。 Use when: 用户说"在 Amazon 上搜 X""谁在卖 X""X 关键词下排名前几""做 X 的竞品有哪些";或要拿到某个关键词的搜索结果页 ASIN 列表作为下游分析输入。 Don't use: 想拿单个 ASIN 的详情(用 get_amazon_product);想要类目热销榜(用 list_bestsellers);想看 Google/外部对该词的需求(用 google_ai_search 或 google_trends)。 Returns (format='json', 默认): data.json[0].data.{ pageIndex, nextPage, keyword, results[{ asin, title, price, star, rating, sales, badge, rank, sponsored, image, delivery }] } — 约 22 行/页。翻页: 用 page 参数(默认 1,从 1 开始);响应里 nextPage 给下一页页码,nextPage=null 表示到底。 Pair with: ↓ 把 results[].asin 喂给 get_amazon_product / get_amazon_reviews 做单品深拆;↓ 同一 keyword 喂给 google_trends 做"内部搜索热度 vs 外部 Google 热度"对比。 Cost: ~1 积点/页, ~5s。翻页只在用户明确要"更多/Top-N(N>22)/全部"时才做,否则首页够用。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordYes搜索关键词。Examples: '蓝牙耳机' / 'wireless earbuds' / 'stanley quencher' / 'iphone 16 case'。
siteNoAmazon 站点。默认 amz_us(美国站)。amz_us
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).
formatNo返回格式。默认 'json'——结构化搜索结果(每条含 asin/title/price/star/rating/sales/badge/rank 等),适合程序处理。需要原始页面阅读时切 'markdown'。json
pageNo页码,从 1 开始。每页约 22 条 ASIN。结合响应里的 pageIndex/nextPage 决定是否继续翻:nextPage 为下一页页码,nextPage=null(或缺失)表示已到最后一页。**只在用户明确要更多/Top-N(N>单页量)/全部时才翻**,否则首页够用。
Behavior5/5

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

With no annotations, the description fully discloses behavior: it performs a real search, returns first-screen results, explains pagination (nextPage, page parameter), cost (~1 credit/page, ~5s), and cautions on when to paginate. This covers all behavioral aspects beyond the schema.

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?

The description is comprehensive but not overly verbose; it front-loads purpose and usage. Each section (use, don't use, returns, pairing, cost) earns its place. Minor redundancy (e.g., pagination repeated) but acceptable given the helpfulness.

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 no output schema, the description details the return structure (data.json fields) and provides enough context for the agent to understand usage, constraints, and downstream integration. It fully compensates for the missing schema.

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?

Although schema coverage is 100% with good descriptions, the main description adds pragmatic context for the 'page' parameter (when to paginate) and clarifies zipcode usage. This exceeds the baseline 3 expected for high schema coverage, but does not fully replace schema detail.

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 clearly states the tool scrapes Amazon search results (SERP) using keywords, listing specific use cases and differentiating from siblings like get_amazon_product and list_bestsellers. The verb 'search' and resource 'Amazon SERP' are precise.

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?

It explicitly provides when-to-use scenarios (e.g., 'search X on Amazon'), when-not-to-use with alternative tools highlighted, and pairing suggestions with get_amazon_product and google_trends. This leaves no ambiguity for the agent.

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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