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pangolinfo

PangolinFo Amazon Ad Tracker & Review Intelligence

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by pangolinfo

google_trends

Compare keyword popularity trends over time and by region. Identify breakout related queries to spot emerging interests.

Instructions

[Google Trends 关键词热度] 时间序列 + 地区热度 + 相关上升查询(含 Breakout 标记)。一次最多 5 个关键词同图对比。 Use when: 用户说"X 关键词最近热度怎么样""A 和 B 哪个更火""有没有季节性""哪些州最爱 X""breakout 上升词""新品方向判断""趋势对比""X 是不是已经过气了"。 Don't use: 想要绝对搜索量(Trends 只给 0-100 相对值);想看商品/链接(用 search_amazon / google_ai_search);只查一个关键词的瞬时值(数据量不够,至少传 2 个对比才有意义)。 Returns: data.json.{ keywordsGeoData[{ keyword, geoMapData[{ geoCode, geoName, value[], formattedValue[], hasData[] }] }], keywordsRankData[{ keyword, rankList[{ rankedKeyword[{ query, value, formattedValue, link, hasData }] }] }], timelineData[{ time, formattedTime, value[], formattedValue[] }], geoMapData[] }, taskId, url。 Pair with: ↑ keywords 来自用户或 search_amazon 找到的核心词;↓ Breakout/上升词可喂回 search_amazon 探索新机会,或喂 filter_niches 看是否成型为 niche。 Cost: ~1.5 积点/次, ~5s。 Tips: timeRange = today 12-m (默认) | today 3-m | today 5-y | all 等;region = ISO 国家码或 'WORLD';language 影响相关查询的语言。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsYes对比的关键词列表(1-5 个)。Examples: ['wireless earbuds', 'bluetooth earbuds'] (同义词对比) / ['stanley quencher', 'yeti rambler', 'hydro flask'] (竞品品牌对比) / ['halloween costume'] (单词看季节性)。
timeRangeNo时间窗口。常用:'today 12-m'(近 12 月,默认,平衡近况和趋势)、'today 3-m'(近 90 天)、'today 5-y'(5 年长期)、'all'(自 2004 起全部)。today 12-m
regionNo地区代码(ISO 国家或 'WORLD' 全球)。常用:'US' / 'GB' / 'DE' / 'JP' / 'CN'。US
languageNo界面语言 BCP-47 代码,影响相关查询的语言。默认 'en-US'。中文用 'zh-CN'。en-US
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses relative 0-100 values, need for at least two keywords for comparison, cost (~1.5 points), and time tips. Missing an explicit read-only declaration, but overall adequate.

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 lengthy but well-structured with clear sections. Front-loaded with purpose then usage, returns, pairing, cost, tips. Slightly verbose but every sentence adds value.

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 complexity (multiple data types) and no output schema, the description provides a solid overview of return structure (keywordsGeoData, timelineData, etc.) and includes cost, tips. Complete for agent decision and invocation.

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% and description adds meaningful context: examples for keywords, default and recommended values for timeRange and region, and language purpose. Goes beyond schema descriptions.

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's purpose: fetching Google Trends time series, regional heat, and related rising queries with breakout markers. It specifies the verb '获取' and resource '关键词热度', and distinguishes from siblings like search_amazon and google_ai_search.

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 provides explicit when-to-use scenarios (e.g., '用户说X关键词最近热度怎么样') and when-not-to-use (e.g., absolute search volume, single keyword). It also mentions pairing with other tools like search_amazon and filter_niches.

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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