linkfox-google-aimode-search

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Original

English

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Translation

Chinese

Google AI Search

This skill calls Google Search in AI Mode to get the AI Overview answer for a single keyword. Only one question per call is supported — there is no multi-turn follow-up within a single request. The response is unstructured Markdown — summarize it directly, do not route it to a data-analysis sandbox.

本技能调用Google搜索的AI Mode，获取单个关键词对应的AI概览回答。每次调用仅支持一个问题——单次请求内不支持多轮跟进。响应为非结构化Markdown格式，请直接进行总结，无需路由至数据分析沙箱。

Core Concepts

核心概念

The tool drives Google's AI Mode (the panel that appears at the top of Google search results and synthesizes an answer with citations):

The required
```
keyword
```
is sent to Google as the query and the AI Overview for it is captured.
Single-round only: each call handles exactly one question. There is no
```
prompts
```
parameter for follow-ups.
For follow-up questions: the agent must summarize the previous AI Overview answer (key points, citations, relevant context) and concatenate it with the new question into a new
```
keyword
```
, then make a fresh API call.
All answers are returned as a single Markdown document under
```
stdout
```
, with citations linked to the source pages.

resultsNum

reports how many AI Overview blocks were rendered;

means the keyword did not trigger an AI Overview on Google for the requested locale.

该工具调用Google的AI Mode（即Google搜索结果顶部显示的、整合带引用来源的回答的面板）：

将必填的
```
keyword
```
作为查询词发送至Google，抓取对应的AI概览内容。
仅支持单轮对话：每次调用仅处理一个问题，无用于跟进的
```
prompts
```
参数。
跟进问题处理方式：Agent需总结之前的AI概览回答（核心要点、引用来源、相关上下文），并将其与新问题拼接成新的
```
keyword
```
，然后发起新的API调用。
所有回答以单个Markdown文档形式返回在
```
stdout
```
字段中，包含指向源页面的引用链接。

resultsNum

字段表示渲染的AI概览区块数量；若为

，则表示该关键词在请求的区域设置下未触发Google的AI概览。

Parameters

参数

Parameter	Type	Required	Description
keyword	string	Yes	Google search keyword. Sent as the `q=` parameter to Google AI Mode. For follow-up questions, the agent should summarize the previous answer and concatenate with the new question into this field.

参数	类型	是否必填	描述
keyword	string	是	Google搜索关键词。作为 `q=` 参数发送至Google AI Mode。对于跟进问题，Agent需总结之前的回答并与新问题拼接后填入此字段。

Response Fields

响应字段

Field	Type	Description
stdout	string	Markdown document with the AI Overview for the keyword, plus inline citation links
sourceUrl	string	The Google AI Mode search URL that was actually requested
resultsNum	integer	Number of AI Overview blocks rendered (0 = keyword did not trigger AI Overview)
code / errcode	string / integer	`200` on success; non-200 indicates a business error
msg / errmsg	string	`ok` on success; otherwise an error description
costTime	integer	API latency in milliseconds
costToken	integer	Tokens consumed (only billed on success)
taskId	string	Upstream task identifier for tracing
type	string	Render hint, fixed value `stdoutWorkbenches`

字段	类型	描述
stdout	string	包含关键词AI概览内容及内嵌引用链接的Markdown文档
sourceUrl	string	实际请求的Google AI Mode搜索URL
resultsNum	integer	渲染的AI概览区块数量（0表示关键词未触发AI概览）
code / errcode	string / integer	成功时为 `200` ；非200表示业务错误
msg / errmsg	string	成功时为 `ok` ；否则为错误描述
costTime	integer	API延迟时间（毫秒）
costToken	integer	消耗的Token数（仅成功调用时计费）
taskId	string	用于追踪的上游任务标识
type	string	渲染提示，固定值为 `stdoutWorkbenches`

API Usage

API使用方法

This tool is exposed via the LinkFox tool gateway. See

references/api.md

for the calling convention, request/response shape, error codes, and a curl example. You can also run

scripts/google_ai_search.py

directly to test it from the command line.

该工具通过LinkFox工具网关对外开放。调用规范、请求/响应格式、错误码及curl示例可查看

references/api.md

。你也可以直接运行

scripts/google_ai_search.py

从命令行测试该工具。

How to Build Queries

查询构建方法

Each call takes a single

keyword

. For follow-up questions, the agent must summarize the previous result and build a new query.

每次调用仅接收一个

keyword

。对于跟进问题，Agent需总结之前的结果并构建新的查询词。

Tips

技巧

Front-load context in
keyword
: include market/region cues when relevant (
```
"open-ear bone-conduction headphones US 2026"
```
) — the AI Overview is sensitive to phrasing.
Match the language to the target market: ask in English for US/UK/AU markets, Japanese for JP, German for DE, etc. — the AI Overview is biased toward the locale's language.
Use natural-language questions: phrasing like "compare against" / "what are the unsolved pain points" elicits richer AI Overview output than single keywords.
For follow-ups, summarize and re-ask: when the user wants to dig deeper, the agent should summarize key points from the previous AI Overview response and concatenate with the new question into a new
```
keyword
```
for a fresh call. Example:
```
"Based on the AI overview that top bone-conduction headphones are Shokz OpenRun Pro and AfterShokz Aeropex, what are the unsolved technical pain points compared to in-ear earbuds?"
```

在
keyword
前端加载上下文：相关时加入市场/区域提示（如
```
"open-ear bone-conduction headphones US 2026"
```
）——AI概览对措辞敏感。
语言匹配目标市场：针对美/英/澳市场用英文提问，日本市场用日文，德国市场用德文等——AI概览会偏向对应区域的语言。
使用自然语言提问：诸如"compare against" / "what are the unsolved pain points"的措辞比单个关键词能触发更丰富的AI概览输出。

跟进问题需总结后重新提问：当用户想要深入了解时，Agent需总结之前AI概览响应的核心要点，并与新问题拼接成新的

keyword

发起新调用。示例：

"Based on the AI overview that top bone-conduction headphones are Shokz OpenRun Pro and AfterShokz Aeropex, what are the unsolved technical pain points compared to in-ear earbuds?"

Usage Examples

使用示例

1. Single-shot AI Overview

json

{
  "keyword": "GaN charger vs traditional charger comparison"
}

2. Cross-border product research

json

{
  "keyword": "best open-ear bone conduction headphones 2026 US"
}

3. Follow-up question (agent summarizes prior result and re-asks in a new call)

First call:

json

{
  "keyword": "best open-ear bone conduction headphones 2026 US"
}

Second call (agent builds context summary + new question):

json

{
  "keyword": "The AI overview mentioned OpenRun Pro and AfterShokz Aeropex as top picks for bone conduction headphones. What unsolved technical pain points still exist compared to in-ear earbuds?"
}

4. Consumer preference snapshot

json

{
  "keyword": "robot vacuum buying preferences 2026 reddit"
}

5. Long-tail keyword exploration for selection

json

{
  "keyword": "smart pet feeder for cats with camera"
}

1. 单次AI概览查询

json

{
  "keyword": "GaN charger vs traditional charger comparison"
}

2. 跨境产品调研

json

{
  "keyword": "best open-ear bone conduction headphones 2026 US"
}

3. 跟进问题（Agent总结之前结果并在新调用中重新提问）

第一次调用：

json

{
  "keyword": "best open-ear bone conduction headphones 2026 US"
}

第二次调用（Agent构建上下文总结+新问题）：

json

{
  "keyword": "The AI overview mentioned OpenRun Pro and AfterShokz Aeropex as top picks for bone conduction headphones. What unsolved technical pain points still exist compared to in-ear earbuds?"
}

4. 消费者偏好快照

json

{
  "keyword": "robot vacuum buying preferences 2026 reddit"
}

5. 长尾关键词选品探索

json

{
  "keyword": "smart pet feeder for cats with camera"
}

Display Rules

展示规则

Render the Markdown directly:
```
stdout
```
is already structured Markdown with headings, bullets, and citation links — preserve that structure when answering the user.
Cite sources: keep the inline reference links from
```
stdout
```
so the user can verify each claim.
Flag empty AI Overview: if
```
resultsNum
```
is
```
0
```
, tell the user Google AI Overview did not trigger for that keyword and suggest rephrasing or trying a different region.
Don't reroute to a data-analysis sandbox: the output is unstructured text and not suitable for SQL-like processing.
Indicate freshness: results reflect Google AI Mode at call time; mention this when the user asks about recency.
Handle business errors: if
```
code
```
/
```
errcode
```
is not
```
200
```
, surface the
```
msg
```
/
```
errmsg
```
to the user and suggest retrying or refining the input.

直接渲染Markdown：
```
stdout
```
已是包含标题、项目符号和引用链接的结构化Markdown——回答用户时请保留该结构。
保留来源引用：保留
```
stdout
```
中的内嵌引用链接，方便用户验证每个结论。
标记空AI概览：若
```
resultsNum
```
为
```
0
```
，告知用户该关键词未触发Google AI概览，并建议重新措辞或尝试其他区域。
不要路由至数据分析沙箱：输出为非结构化文本，不适合类SQL处理。
说明时效性：结果反映调用时的Google AI Mode状态，当用户询问时效性时需提及这一点。
处理业务错误：若
```
code
```
/
```
errcode
```
不为
```
200
```
，将
```
msg
```
/
```
errmsg
```
告知用户，并建议重试或优化输入内容。

Important Limitations

重要限制

Unstructured output: Markdown text only — no structured tables, no second-pass data query.
AI Overview not guaranteed: some keywords (especially niche, ambiguous, or sensitive ones) do not trigger AI Overview at all (
```
resultsNum = 0
```
).
Single-round only: no multi-turn follow-up within one call. For follow-ups, the agent must summarize previous context and make a new call.
Locale follows Google's defaults: the tool uses Google's standard AI Mode endpoint without an explicit region switch; bias the language and wording of
```
keyword
```
to match the market you care about.
Real-time fetch: results are pulled live, so output for the same keyword can vary across calls.

非结构化输出：仅为Markdown文本——无结构化表格，无二次数据查询功能。
AI概览不保证触发：部分关键词（尤其是小众、模糊或敏感的关键词）完全不会触发AI概览（
```
resultsNum = 0
```
）。
仅支持单轮对话：单次调用内不支持多轮跟进。如需跟进，Agent必须总结之前的上下文并发起新调用。
区域遵循Google默认设置：该工具使用Google标准AI Mode端点，无显式区域切换功能；需调整
```
keyword
```
的语言和措辞以匹配目标市场。
实时抓取：结果为实时获取，同一关键词的输出可能因调用时间不同而有所差异。

User Expression & Scenario Quick Reference

用户表述与场景速查

Applicable — when the user wants AI-summarized live web information:

User Says	Scenario
"用 Google AI 帮我搜...", "Google AI Overview 看下..."	Direct AI Overview lookup
"海外消费者对 XX 怎么看", "美国市场对 XX 的偏好"	Cross-border consumer preference
"XX 的最新趋势 / 痛点 / 使用场景"	Deep research
"顺便问一下 / 然后再追问 ..."	Follow-up needed (agent summarizes prior result and re-asks in new call)
"网页上对 XX 的总结", "搜索引擎里大家怎么说 XX"	Web-wide summarization
"长尾选品调研 / 蓝海选品方向"	Long-tail product exploration

Not applicable — better routed elsewhere:

Querying internal structured datasets (use the appropriate data query tool).
Amazon ABA search-term analytics (use the ABA data explorer).
Pulling structured product listings, prices, reviews from a specific platform (use the matching platform skill).
Plain web search where the user only needs raw page content with no AI synthesis (use the standard web search skill).
Image generation, image recognition, or file analysis.

Boundary judgment: when the user wants "AI 帮我汇总网上的说法" or "用谷歌搜一下", this skill applies. If the user wants to ask follow-up questions, the agent should summarize the previous answer and make a new call. If they explicitly want raw search results, structured data, or already have a specialized data source, do not use this skill.

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.

适用场景——当用户需要AI总结的实时网页信息时：

用户表述	场景
"用 Google AI 帮我搜...", "Google AI Overview 看下..."	直接AI概览查询
"海外消费者对 XX 怎么看", "美国市场对 XX 的偏好"	跨境消费者偏好分析
"XX 的最新趋势 / 痛点 / 使用场景"	深度调研
"顺便问一下 / 然后再追问 ..."	需要跟进（Agent总结之前结果并在新调用中重新提问）
"网页上对 XX 的总结", "搜索引擎里大家怎么说 XX"	全网内容总结
"长尾选品调研 / 蓝海选品方向"	长尾产品探索

不适用场景——应路由至其他工具：

查询内部结构化数据集（使用对应的数据查询工具）。
Amazon ABA搜索词分析（使用ABA数据探索工具）。
从特定平台抓取结构化产品列表、价格、评论（使用匹配的平台技能）。
用户仅需原始页面内容无需AI合成的普通网页搜索（使用标准网页搜索技能）。
图片生成、图像识别或文件分析。

边界判断：当用户提出“AI 帮我汇总网上的说法”或“用谷歌搜一下”时，适用本技能。若用户想要跟进提问，Agent需总结之前的回答并发起新调用。若用户明确需要原始搜索结果、结构化数据或已有特定数据源，则不使用本技能。

反馈：

当出现以下任一情况时，自动检测并通过Feedback API提交反馈：

本技能描述的功能或用途与实际行为不符
技能结果与用户意图不匹配
用户表达了对本技能的不满或赞赏
任何你认为可以改进的内容

按照

references/api.md

中的说明调用反馈API，请勿中断用户流程。

Handling Large Responses

大响应处理

To avoid overflowing the agent context, persist the response to disk and extract only the fields you need:

python scripts/response_io.py run --script scripts/google_ai_search.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>"   # or --path "<JMESPath>"

Pick
--out-dir
outside any git working tree (e.g.
/tmp/...
on Unix,
%TEMP%/...
on Windows). Persisted responses may contain PII, pricing, or auth-sensitive data — do not commit them. Files are not auto-deleted; clean up when the task is done.

run

writes the full response to a file and emits only a schema preview + file path.

read

projects specific fields, with

--limit/--offset

for slicing and

--format json|jsonl|csv|table

for output.

When to prefer this pattern — apply your judgment based on the response characteristics, e.g.:

High field count per record, or fields you don't need
Batch/paginated results (multiple items per call)
Long-text fields (descriptions, reviews, HTML, time series)
Output reused across later steps rather than consumed immediately

For small, single-use responses, calling the main script directly is fine.

⚠️ The preview is a truncated schema + sample, not the full data. Any field-level decision must read from the persisted file via

read

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

为避免超出Agent上下文限制，请将响应持久化到磁盘并仅提取所需字段：

python scripts/response_io.py run --script scripts/google_ai_search.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>"   # or --path "<JMESPath>"

请选择Git工作区外的
--out-dir
（如Unix系统的
/tmp/...
，Windows系统的
%TEMP%/...
）。持久化的响应可能包含个人身份信息（PII）、定价或敏感认证数据——请勿提交到代码仓库。文件不会自动删除，任务完成后请清理。

run

命令将完整响应写入文件，并仅输出架构预览+文件路径。

read

命令提取特定字段，支持

--limit/--offset

进行切片，以及

--format json|jsonl|csv|table

指定输出格式。

何时优先使用此模式——根据响应特征判断，例如：

每条记录字段数量多，或包含不需要的字段
批量/分页结果（每次调用返回多个条目）
长文本字段（描述、评论、HTML、时间序列）
输出需在后续步骤复用而非立即使用

对于小型、一次性响应，直接调用主脚本即可。

⚠️ 预览内容是截断的架构+示例，而非完整数据。任何字段层面的决策必须通过

read

命令从持久化文件中读取。

如需更多高质量、专业的跨境电商技能，请访问LinkFox Skills。