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Skillslinkfox-google-aimode-search

linkfox-google-aimode-search

Agents & models

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Updated 10-16 00:00

About this skill

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.

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):

  1. The required keyword is sent to Google as the query and the AI Overview for it is captured.
  2. Single-round only: each call handles exactly one question. There is no prompts parameter for follow-ups.
  3. 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.
  4. 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; 0 means the keyword did not trigger an AI Overview on Google for the requested locale.

Parameters

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.

Response Fields

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

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

Each call takes a single keyword. For follow-up questions, the agent must summarize the previous result and build a new query.

Tips

  1. 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.
  2. 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.
  3. Use natural-language questions: phrasing like "compare against" / "what are the unsolved pain points" elicits richer AI Overview output than single keywords.
  4. 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?"

Usage Examples

1. Single-shot AI Overview

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

2. Cross-border product research

{
  "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:

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

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

{
  "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

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

5. Long-tail keyword exploration for selection

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

Display Rules

  1. Render the Markdown directly: stdout is already structured Markdown with headings, bullets, and citation links — preserve that structure when answering the user.
  2. Cite sources: keep the inline reference links from stdout so the user can verify each claim.
  3. 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.
  4. Don't reroute to a data-analysis sandbox: the output is unstructured text and not suitable for SQL-like processing.
  5. Indicate freshness: results reflect Google AI Mode at call time; mention this when the user asks about recency.
  6. Handle business errors: if code / errcode is not 200, surface the msg / errmsg to 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).
  • 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.

User Expression & Scenario Quick Reference

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

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
"By the way, ask / then follow up with..." Follow-up needed (agent summarizes prior result and re-asks in new call)
"Summary of XX on web pages", "What people say 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 people are saying online" or "search it on Google", 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:

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

Call the feedback API as specified in references/api.md. Do not interrupt the user's flow.

Handling Large Responses

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.