This skill calls Google Search in AI Mode to get the AI Overview answer for a keyword and follow up with up to several additional questions in a single round trip. The response is unstructured Markdown — summarize it directly, do not route it to a data-analysis sandbox.
Core Concepts
The tool drives Google's AI Mode (the panel that appears at the top of Google search results and synthesizes an answer with citations) and stitches multi-turn follow-ups into one call:
- The required
keywordis sent to Google as the initial query and the AI Overview for it is captured first. - Each entry in the optional
promptsarray is asked as a follow-up question in the same AI conversation, in order. - All answers are concatenated into a single Markdown document under
stdout, with each section clearly labelled and citations linked to the source pages.
resultsNum reports how many AI Overview blocks were rendered; 0 means the keyword did not trigger an AI Overview on Google for the requested locale.
Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
| keyword | string | Yes | Initial Google search keyword (≤ 1000 chars). Sent as the q= parameter to Google AI Mode. |
| prompts | string[] | No | Follow-up questions for additional turns of the same AI conversation. Recommended ≤ 5 entries; more is allowed but response time degrades sharply. Omit this field for a single-shot AI Overview lookup with no follow-ups. |
Response Fields
| Field | Type | Description |
|---|---|---|
| stdout | string | Markdown document with the AI Overview for the keyword and each follow-up answer in order, 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 |
API Usage
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.
How to Build Queries
The two inputs work together: keyword is the entry point, prompts are the follow-ups. Treat them as one continuous AI conversation, not as independent searches.
Tips
- 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. - Keep
promptsfocused and ordered: each follow-up reuses the previous turn's context, so cheaper questions go first (e.g. "what are the main use cases?" before "what are the unsolved technical pain points?"). - Limit follow-ups to 3–5: more turns dramatically increase latency without proportional value.
- 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 in
prompts: phrasing like "compare against" / "what are the unsolved pain points" elicits richer AI Overview output than single keywords.
Usage Examples
1. Single-shot AI Overview (no follow-ups — prompts omitted)
Pass keyword only when the user just wants the AI Overview for one query, with no multi-turn follow-up. prompts is optional and can be left out entirely:
{
"keyword": "GaN charger vs traditional charger comparison"
}
2. Cross-border product research with follow-ups
{
"keyword": "best open-ear bone conduction headphones 2026 US",
"prompts": [
"What are the main use cases consumers care about?",
"What unsolved technical pain points still exist compared to in-ear earbuds?"
]
}
3. Consumer preference snapshot
{
"keyword": "robot vacuum buying preferences 2026 reddit",
"prompts": [
"Which features get praised most in user reviews?",
"Which complaints come up repeatedly?"
]
}
4. Long-tail keyword exploration for selection
{
"keyword": "smart pet feeder for cats with camera",
"prompts": [
"What price ranges are mentioned most often?",
"Which brands appear in the top picks?"
]
}
Display Rules
- Render the Markdown directly:
stdoutis already structured Markdown with headings, bullets, and citation links — preserve that structure when answering the user. - Cite sources: keep the inline reference links from
stdoutso the user can verify each claim. - Flag empty AI Overview: if
resultsNumis0, 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/errcodeis not200, surface themsg/errmsgto the user and suggest retrying or refining the input.
Important Limitations
- 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). - Latency scales with
promptslength: each follow-up is an additional AI turn on Google; 5+ prompts can take tens of seconds. - 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
keywordto match the market you care about. - Real-time fetch: results are pulled live, so output for the same keyword can vary across calls.
User Expression & Scenario Quick Reference
Applicable — when the user wants AI-summarized live web information with multi-turn depth:
| User Says | Scenario |
|---|---|
| "Use Google AI to search for me...", "Take a look at Google AI Overview..." | Direct AI Overview lookup |
| "What do overseas consumers think about XX", "Preferences for XX in the US market" | Cross-border consumer preference |
| "The latest trends / pain points / use cases of XX" | Deep research with follow-ups |
| "By the way, ask / then follow up with..." | Multi-turn follow-up needed |
| "A summary of XX on the web", "What people are saying about XX in search engines" | Web-wide summarization |
| "Long-tail product selection research / blue ocean product selection directions" | 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 to help me summarize what is being said online" or "search with Google and follow up for a few rounds", this skill applies. 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.
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