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pangolinfo

PangolinFo Amazon Ad Tracker & Review Intelligence

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by pangolinfo

google_ai_search

Fetches Google search results with AI Overview summaries, organic listings, and related searches to uncover consumer voice and external demand for product research.

Instructions

[Google SERP + AI Overview 抓取] 抓 Google 搜索结果,含顶部 AI Overview 摘要、organic 自然位、相关搜索词。两种模式:overview(标准 SERP)/ ai_mode(沉浸式对话,支持多轮追问)。 Use when: 用户说"Google 搜一下""外部需求""市场上人们怎么说 X""Reddit/Quora 上的痛点""AI 搜索时代我的内容能被引用吗""为某关键词找用户原声";选品 SOP 里的"消费者原声"步骤;判断新品概念在 Amazon 站外是否有真实需求。 Don't use: 想在 Amazon 站内搜(用 search_amazon);只要趋势曲线(用 google_trends,更便宜更聚焦)。 Returns: data.{ results_num, ai_overview, json.items[ { type:'ai_overview', items:[{content:[...], references:[{title,url,domain}]}] }, { type:'organic', items:[{title,url,text}] }, { type:'related_searches', items:[...] } ], screenshot, taskId }。 Pair with: ↑ query 由用户提问推导;mode='ai_mode' 时传 followups[1..5] 做多轮追问;↓ ai_overview.references[].url 可作外部权威源,organic 结果可喂下游做内容竞争分析。 Cost: ~2 积点/次, ~30s(——这是 Google AI 渲染时间)。 Tips: 单次查询用 overview 更经济;只有要"拆解复杂问题 + 连续追问"时才上 ai_mode。followups 超 5 条响应明显变慢。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes搜索关键词或问题。Examples: 'wireless earbuds reviews' (单点查询) / 'how does noise cancellation work' (问句) / 'what do people complain about Stanley Quencher' (用户痛点)。
modeNo搜索模式:'overview'(默认)= 标准 Google SERP + 顶部 AI Overview 摘要,适合一次性查询;'ai_mode' = Google AI Mode 沉浸式搜索(udm=50),适合复杂问题拆解和多轮追问。overview
followupsNo多轮追问列表(仅 mode='ai_mode' 时生效)。每条是基于前一轮答案的追问。**超过 5 条响应效率显著下降**。
screenshotNo是否返回搜索页截图 URL。默认 false。
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses cost (~2 积点/次), slowness (~30s), mode differences, followups limit, and performance tips. However, it lacks explicit mention of authentication requirements or rate limits, which slightly reduces transparency.

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 well-structured with labeled sections (Use when, Don't use, Returns, Pair with, Cost, Tips) and front-loaded with the core purpose. However, it is slightly verbose with multiple language examples and redundant phrasing (e.g., '选品 SOP 里的'消费者原声'步骤'). Every sentence adds value, but conciseness could be improved.

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 4 parameters, no output schema, and no annotations, the description covers all essential aspects: return format with nested structure, cost and latency, mode differences, followups constraints, and usage tips. It is fully self-contained for an agent to use 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 significant value beyond the schema: it provides concrete examples for query (e.g., 'wireless earbuds reviews'), clarifies mode semantics with 'standard SERP' vs 'immersive dialogue', warns about followups performance degradation, and explains the screenshot parameter. This enriches parameter understanding.

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 fetches Google SERP with AI Overview, organic results, and related searches. It specifies two distinct modes (overview and ai_mode) and contrasts with sibling tools like search_amazon and google_trends, making its purpose unambiguous.

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?

The description provides explicit 'Use when' scenarios (e.g., 'Google 搜一下', '外部需求') and 'Don't use' conditions (e.g., Amazon search, trends only). It also includes pairing hints with other tools, offering comprehensive guidance on when to use this tool versus alternatives.

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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