linkfox-junglescout-keyword-history

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Original

English

🇨🇳

Translation

Chinese

Jungle Scout — 关键词历史搜索量

Jungle Scout — Historical Keyword Search Volume

This skill queries the historical exact search volume for Amazon keywords via the Jungle Scout data source, returning weekly search volume data points over a specified date range across 10 Amazon marketplaces.

Core Concepts

Jungle Scout 关键词历史搜索量工具提供亚马逊各站点关键词的周维度精确匹配搜索量历史数据。卖家可以通过查询指定时间范围内的搜索量变化来判断：

季节性规律：关键词在哪些月份是旺季/淡季
趋势方向：搜索量是持续上升、下降还是平稳
波动幅度：判断市场需求的稳定性
节假日效应：大促、节日前后的搜索量飙升

数据粒度：每条记录代表一个 7 天周期，包含该周内的精确匹配搜索量估算值。

The Jungle Scout Historical Keyword Search Volume tool provides historical data on weekly exact-match search volume for keywords across Amazon marketplaces. Sellers can determine the following by querying search volume changes within a specified time range:

Seasonal patterns: Which months are peak/off-peak seasons for the keyword
Trend direction: Whether search volume is continuously rising, falling, or stable
Fluctuation range: Judge the stability of market demand
Holiday effect: Surge in search volume before and after major promotions/festivals

Data granularity: Each record represents a 7-day cycle, containing the estimated exact-match search volume for that week.

Data Fields

Output Fields

Field	API Name	Description	Example
周期标识	id	数据周期标识（市场/关键词/日期范围）	us_sushi_20250105_20250111
周期开始日期	estimateStartDate	7天统计周期的起点	2025-01-05
周期结束日期	estimateEndDate	7天统计周期的终点	2025-01-11
精确搜索量	estimatedExactSearchVolume	该周期内精确匹配搜索量（次/周）	12500
资源类型	type	固定值	historical_keyword_search_volume
消耗Token	costToken	本次调用消耗的 token 数	1

Field	API Name	Description	Example
Cycle ID	id	Data cycle identifier (marketplace/keyword/date range)	us_sushi_20250105_20250111
Cycle Start Date	estimateStartDate	Start of the 7-day statistical cycle	2025-01-05
Cycle End Date	estimateEndDate	End of the 7-day statistical cycle	2025-01-11
Exact Search Volume	estimatedExactSearchVolume	Exact-match search volume within the cycle (times/week)	12500
Resource Type	type	Fixed value	historical_keyword_search_volume
Token Cost	costToken	Number of tokens consumed for this call	1

Supported Marketplaces

站点	marketplace 值	说明
美国	us	Amazon.com
英国	uk	Amazon.co.uk
德国	de	Amazon.de
印度	in	Amazon.in
加拿大	ca	Amazon.ca
法国	fr	Amazon.fr
意大利	it	Amazon.it
西班牙	es	Amazon.es
墨西哥	mx	Amazon.com.mx
日本	jp	Amazon.co.jp

默认站点为 us。当用户未指定站点时，使用 us。

Marketplace	marketplace Value	Description
United States	us	Amazon.com
United Kingdom	uk	Amazon.co.uk
Germany	de	Amazon.de
India	in	Amazon.in
Canada	ca	Amazon.ca
France	fr	Amazon.fr
Italy	it	Amazon.it
Spain	es	Amazon.es
Mexico	mx	Amazon.com.mx
Japan	jp	Amazon.co.jp

The default marketplace is us. If users do not specify a marketplace, use us.

API Usage

This tool calls the LinkFox tool gateway API. See

references/api.md

for calling conventions, request parameters, and response structure. You can also execute

scripts/junglescout_keyword_history.py

directly to run queries.

This tool calls the LinkFox tool gateway API. See

references/api.md

for calling conventions, request parameters, and response structure. You can also execute

scripts/junglescout_keyword_history.py

directly to run queries.

How to Build Queries

所有四个参数均为必填：

marketplace

、

keyword

、

startDate

、

endDate

。

All four parameters are required:

marketplace

keyword

startDate

endDate

Principles for Building API Calls

站点映射：用户说"美国站"→
```
us
```
，"日本站"→
```
jp
```
，"德国站"→
```
de
```
；未指定时默认
```
us
```
日期格式：必须为
```
YYYY-MM-DD
```
，如
```
2025-01-05
```
时间跨度：
```
startDate
```
到
```
endDate
```
最长 366 天；超过时需拆分为多次请求
关键词：原样传入用户提供的关键词（英文小写为佳）
常用时间推算：
- "过去3个月" → endDate 取今天，startDate 取约90天前
- "去年全年" →
```
2025-01-01
```
  到
```
2025-12-31
```
- "旺季" → 根据品类判断，如 Q4 为
```
10-01
```
  到
```
12-31
```

Marketplace Mapping: When users say "US marketplace" →
```
us
```
, "Japan marketplace" →
```
jp
```
, "Germany marketplace" →
```
de
```
; use
```
us
```
by default if not specified
Date Format: Must be
```
YYYY-MM-DD
```
, e.g.,
```
2025-01-05
```
Time Span: The maximum span from
```
startDate
```
to
```
endDate
```
is 366 days; split into multiple requests if exceeded
Keyword: Pass the user-provided keyword as-is (preferably in lowercase English)
Common Time Calculations:
- "Past 3 months" → set endDate to today, startDate to approximately 90 days ago
- "Whole last year" →
```
2025-01-01
```
  to
```
2025-12-31
```
- "Peak season" → Determine based on category, e.g., Q4 is
```
10-01
```
  to
```
12-31
```

Common Query Scenarios

1. 查看关键词近半年搜索趋势

json

{
  "marketplace": "us",
  "keyword": "yoga mat",
  "startDate": "2025-10-01",
  "endDate": "2026-03-31"
}

2. 判断关键词季节性（查全年数据）

json

{
  "marketplace": "us",
  "keyword": "christmas decorations",
  "startDate": "2025-01-01",
  "endDate": "2025-12-31"
}

3. 对比旺季与淡季搜索量

分两次调用：

淡季：
```
startDate=2025-02-01
```
,
```
endDate=2025-04-30
```
旺季：
```
startDate=2025-10-01
```
,
```
endDate=2025-12-31
```

4. 多站点对比

对同一关键词分别查询不同 marketplace（如

us

、

de

、

jp

），比较各站搜索量规模。

5. 验证市场需求是否增长

json

{
  "marketplace": "de",
  "keyword": "luftreiniger",
  "startDate": "2025-04-01",
  "endDate": "2026-03-31"
}

1. View search trend of a keyword in the past six months

json

{
  "marketplace": "us",
  "keyword": "yoga mat",
  "startDate": "2025-10-01",
  "endDate": "2026-03-31"
}

2. Determine keyword seasonality (query annual data)

json

{
  "marketplace": "us",
  "keyword": "christmas decorations",
  "startDate": "2025-01-01",
  "endDate": "2025-12-31"
}

3. Compare search volume between peak and off-peak seasons

Call twice:

Off-peak season:
```
startDate=2025-02-01
```
,
```
endDate=2025-04-30
```
Peak season:
```
startDate=2025-10-01
```
,
```
endDate=2025-12-31
```

4. Multi-marketplace comparison

Query the same keyword across different marketplaces (e.g.,

us

de

jp

) to compare search volume scales across each marketplace.

5. Verify if market demand is growing

json

{
  "marketplace": "de",
  "keyword": "luftreiniger",
  "startDate": "2025-04-01",
  "endDate": "2026-03-31"
}

Display Rules

趋势可视化优先：建议以时间线/折线图方式展示搜索量变化，横轴为日期周期，纵轴为搜索量
表格辅助：同时提供数据表格供精确查阅，列包括：周期开始日期、周期结束日期、搜索量
趋势总结：在数据之后简要总结趋势方向（上升/下降/平稳/周期性波动），标注峰值和谷值周期
峰值标注：高亮搜索量最高和最低的周期，便于用户快速判断旺淡季
Error handling: When a query fails, explain the reason based on the error response and suggest adjusting parameters（如日期范围超 366 天）

Prioritize trend visualization: It is recommended to display search volume changes in a timeline/line chart, with the horizontal axis representing date cycles and the vertical axis representing search volume
Table assistance: Provide a data table for precise reference at the same time, including columns: Cycle Start Date, Cycle End Date, Search Volume
Trend summary: Briefly summarize the trend direction (rising/falling/stable/periodic fluctuation) after the data, and mark peak and trough cycles
Peak marking: Highlight the cycles with the highest and lowest search volume to help users quickly identify peak and off-peak seasons
Error handling: When a query fails, explain the reason based on the error response and suggest adjusting parameters (e.g., date range exceeds 366 days)

Important Limitations

时间跨度上限：单次查询
```
startDate
```
到
```
endDate
```
最长 366 天，超过需拆分查询
数据粒度：周维度（7天一个数据点），非日维度
搜索量类型：精确匹配搜索量（Exact Match），非广泛匹配
所有参数必填：
```
marketplace
```
、
```
keyword
```
、
```
startDate
```
、
```
endDate
```
缺一不可

Time span limit: The maximum span from
```
startDate
```
to
```
endDate
```
for a single query is 366 days; split into multiple queries if exceeded
Data granularity: Weekly (one data point per 7 days), not daily
Search volume type: Exact Match search volume, not Broad Match
All parameters required:
```
marketplace
```
,
```
keyword
```
,
```
startDate
```
,
```
endDate
```
are all mandatory

User Expression & Scenario Quick Reference

Applicable - 关键词搜索量历史趋势分析：

User Says	Scenario
"这个词搜索量怎么变化的"	搜索量趋势查询
"这个品类有没有季节性"	全年数据判断季节规律
"搜索量最近在涨还是跌"	近期趋势判断
"什么时候是旺季"	峰值周期识别
"去年Q4搜索量多少"	指定时间段搜索量查询
"这个词在德国站热不热"	非美国站搜索量查询
"对比两个时间段的搜索量"	旺淡季/同比对比

Not applicable - 超出关键词历史搜索量范围：

关键词建议/拓词（需要关键词挖掘工具）
实时/当前搜索量排名（需要 ABA 或 SIF 工具）
关键词竞争度、CPC 出价
商品销量、listing 分析
非亚马逊平台的搜索量

Boundary judgment: When users say "搜索量", "关键词热度", or "市场需求趋势", if they specifically want to see how a keyword's search volume changes over a period of time (historical trend), this skill applies. If they want the current ranking or a list of trending keywords, it does not apply.

Feedback:

Auto-detect and report feedback via the Feedback API when any of the following apply:

The functionality or purpose described in this skill does not match actual behavior
The skill's results do not match the user's intent
The user expresses dissatisfaction or praise about this skill
Anything you believe could be improved

Call the feedback API as specified in

references/api.md

. Do not interrupt the user's flow.

For more high-quality, professional cross-border e-commerce skills, visit LinkFox Skills.

Applicable - Historical trend analysis of keyword search volume:

User Says	Scenario
"How has the search volume of this keyword changed?"	Search volume trend query
"Does this category have seasonality?"	Judge seasonal patterns with annual data
"Is the search volume rising or falling recently?"	Recent trend judgment
"When is the peak season?"	Peak cycle identification
"What was the search volume in Q4 last year?"	Search volume query for a specified time period
"Is this keyword popular on the German marketplace?"	Search volume query for non-US marketplace
"Compare search volumes of two time periods"	Peak/off-peak season/year-over-year comparison

Not applicable - Beyond the scope of historical keyword search volume:

Keyword suggestions/expansion (requires keyword research tools)
Real-time/current search volume ranking (requires ABA or SIF tools)
Keyword competitiveness, CPC bidding
Product sales, listing analysis
Search volume on non-Amazon platforms

Boundary judgment: When users say "search volume", "keyword popularity", or "market demand trend", if they specifically want to see how a keyword's search volume changes over a period of time (historical trend), this skill applies. If they want the current ranking or a list of trending keywords, it does not apply.

Feedback:

Auto-detect and report feedback via the Feedback API when any of the following apply:

The functionality or purpose described in this skill does not match actual behavior
The skill's results do not match the user's intent
The user expresses dissatisfaction or praise about this skill
Anything you believe could be improved

Call the feedback API as specified in

references/api.md

. Do not interrupt the user's flow.

For more high-quality, professional cross-border e-commerce skills, visit LinkFox Skills.