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by one.aisa
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Query a Google AI answer engine (AI Overviews / AI Mode) for GEO/AEO visibility.

post_oxylabs_ai_search
Read-onlyIdempotent

Synchronous passthrough to the upstream Oxylabs Realtime endpoint (POST /v1/queries) for the Google answer engines — Google AI Overviews (source: google_search) and Google AI Mode (source: google_ai_mode). Send query with render: "html", parse: true, and a country-level geo_location; the request body is passed through unchanged. The response returns the AI-generated answer text and the cited source URLs. Billed a flat $0.001 per successful result; 400/429/5xx/6xx and upstream 4xx responses are not billed. Google-type sources take ~4–8s, so use a client timeout of at least 30s.

LLM sources (ChatGPT, Gemini, Perplexity) are no longer served here — Oxylabs moved them to an asynchronous Push-Pull flow. Use post_oxylabs_llm (POST /oxylabs/llm) plus get_oxylabs_llm_job for those sources; calling this endpoint with source: chatgpt|gemini|perplexity returns HTTP 422 "Realtime integration is not supported for LLM sources. Please use Push-Pull."

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
parseNoReturn structured, parsed results instead of raw output. Recommended for every source.
queryNoThe search query. Required for `google_search` and `google_ai_mode`.
renderNoFor `google_search` and `google_ai_mode`, set to "html" to render the page before parsing.
sourceYesThe Google AI answer engine to query. `google_search` returns Google AI Overviews; `google_ai_mode` returns Google AI Mode. For ChatGPT/Gemini/Perplexity use the async endpoint `post_oxylabs_llm` instead.
geo_locationNoCountry-level geo-location for the query, e.g. "United States".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changed
    • removedInput schema / properties / prompt
      Removed value: -{
-  "description": "The natural-language prompt. Used by `chatgpt` (max 4000 chars), `gemini` (max 8000 chars), and `perplexity`. Use `query` instead for the Google-type sources.",
-  "example": "best noise cancelling headphones 2026",
-  "type": "string"
-}
    • changedInput schema / properties / query / description
      Previous value: -"The search query. Used by `google_search` and `google_ai_mode`. Use `prompt` instead for chatgpt/gemini/perplexity."New value: +"The search query. Required for `google_search` and `google_ai_mode`."
    • removedInput schema / properties / search
      Removed value: -{
-  "description": "For `chatgpt`, set to true to have ChatGPT browse the web before answering.",
-  "example": true,
-  "type": "boolean"
-}
    • changedInput schema / properties / source / description
      Previous value: -"The AI answer engine to query. `google_search` returns Google AI Overviews. Each source expects a specific subset of the parameters below."New value: +"The Google AI answer engine to query. `google_search` returns Google AI Overviews; `google_ai_mode` returns Google AI Mode. For ChatGPT/Gemini/Perplexity use the async endpoint `post_oxylabs_llm` instead."
    • changedInput schema / properties / source / enum
      Previous value: -[
-  "chatgpt",
-  "gemini",
-  "perplexity",
-  "google_search",
-  "google_ai_mode"
-]New value: +[
+  "google_search",
+  "google_ai_mode"
+]
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds significant behavioral context beyond these: cost per successful result ($0.001), non-billing on certain error codes, typical latency (4–8s), the passthrough nature of the request body, and the returned content (AI answer text and cited URLs). It also discloses the 422 error for unsupported sources, all of which materially affect how an agent should use the tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact but information-dense: it opens with the core purpose and endpoint, then covers required fields, pricing, latency, error behavior, and explicit alternatives in a logical flow. Every sentence carries unique value, and the exclusion of LLM sources is front-loaded so the agent doesn't have to read far to avoid a wrong call.

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?

For a tool with this complexity—passthrough behavior, cost, latency, error handling, and a clear sibling alternative—the description covers all essential aspects an agent needs to call it correctly and safely. It also references the output (AI answer text and cited URLs), and with an output schema present, the agent has complete information to integrate it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% so the baseline is 3, but the description adds value by prescribing how to combine parameters ('Send query with render: "html", parse: true, and a country-level geo_location') and by explaining the meaning of the source values. While the schema already documents each parameter, the description gives practical usage guidance that clarifies expected values and their interplay.

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 states a specific verb ('query'), a precise resource (Google AI answer engines: AI Overviews and AI Mode), and the exact upstream endpoint. It clearly distinguishes this tool from the related LLM tools by explicitly naming post_oxylabs_llm and get_oxylabs_llm_job as the correct alternatives, so an agent can immediately tell what this tool is for.

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?

The description provides explicit when-to-use and when-not-to-use guidance: it states that LLM sources are no longer served and instructs to use post_oxylabs_llm instead, including the exact HTTP 422 error that will be returned if misused. It also gives operational guidance on required fields (render, parse, geo_location) and a minimum client timeout of 30s based on expected latency, leaving no ambiguity about when and how to invoke it.

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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