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ai_visibility_search

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Compare answers from multiple AI engines to identify consensus and outliers. Returns a synthesis of agreements and divergences for brand AI-SEO visibility checks.

Instructions

Ask multiple AI answer engines (ChatGPT, Gemini, Google AI Overview) the same question and compare their answers. Returns one result per engine — status, an answer summary, latency, credits used, and a consensus/outlier verdict — plus an AI-generated synthesis of where the engines agree and diverge. Async; typically completes within 1–2 minutes and this tool polls to completion. Use for brand/AI-SEO visibility checks ("what do AI engines say about X"), answer comparison, and geo-specific AI answers (set country). Billed per source at that Wire action's rate; failed sources are free. Set include_full_content=true only when you need each engine's raw full answer — it is large.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	Yes	The question to ask every engine (max 2000 characters).
`country`	No	Two-letter ISO country for the search geography (proxy exit). Defaults to "us".	us
`sources`	No	Engine slugs to query (see ai_visibility_sources). Omit to query all enabled engines.
`include_full_content`	No	Include each engine's raw full answer in the results (large). Defaults to false — summaries and the synthesis are returned regardless.

Output Schema

TableJSON Schema

Name	Required	Description
`status`	Yes
`country`	No
`results`	Yes
`search_id`	Yes
`synthesis`	No	AI-generated synthesis of where the engines agree and diverge.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.4.1Sep 22, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description reveals important behavior beyond the annotations: async execution, polling to completion, typical 1–2 minute duration, per-source billing, free failed sources, and the large size of full content. This is exactly the operational context an agent needs for a non-trivial async 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?

Four dense sentences, each earning its place: purpose and output, asynchronous behavior, use cases, and billing/full-content caveats. The most important information is front-loaded, and the structure is easy to parse despite the level of detail.

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?

Given the tool's complexity — async, multi-source, billed, with a detailed output — the description covers all non-obvious aspects: timing, polling, cost, failure handling, and the large-response caveat. The output schema handles return-value documentation, so nothing critical is missing.

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. The description adds value by warning to set include_full_content=true only when the raw full answer is needed and noting that it is large, plus clarifying the country parameter's role in geo-specific answers. This goes slightly beyond the schema.

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 and resource: 'Ask multiple AI answer engines (ChatGPT, Gemini, Google AI Overview) the same question and compare their answers.' It also details the output shape and clearly distinguishes this from general search tools by naming the engines and the comparison/consensus angle.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit use cases are given: brand/AI-SEO visibility checks, answer comparison, and geo-specific AI answers. The description does not name sibling alternatives like search or agentic_search, nor does it state when NOT to use this tool, but the use-case guidance is clear and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.