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AI Visibility Questions

generate_ai_visibility_questions

Generate the AI-visibility questions for any brand from its website: the kinds of questions real customers ask ChatGPT, Perplexity, and Google when choosing in that market, mapped to the six ANSWER categories (Appearance, Nomination, Showdown, Worth, Expertise, Reputation). Useful for AEO and GEO research and content planning. It reads the site to infer the brand; pass brandName and description if the site cannot be read automatically.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
websiteYesThe brand's website or domain, e.g. example.com. Required.
brandNameNoOptional. Only needed if the site cannot be read automatically (a JavaScript-heavy or very small site).
descriptionNoOptional. One line on what the business sells. Only needed if the site cannot be read automatically.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedInput schema / properties / email
      Removed value: -{
-  "description": "Optional. Provide it to unlock all 50 questions: they are returned in full here and also emailed. No password or signup step.",
-  "type": "string"
-}
    • removedInput schema / properties / includeWhitepaper
      Removed value: -{
-  "description": "Optional, default true. When an email is given, also email the free ANSWER framework whitepaper to that same address (no need to collect the email again). Set false to skip it.",
-  "type": "boolean"
-}
  2. Added

TDQS

A4.4/5.0
Behavior4/5

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

Adds the behavioral detail that it reads the website to infer the brand and describes the fallback when reading fails. Annotations declare non-destructive; description does not claim read-only, so no contradiction. Extra context about site reading is valuable beyond annotations.

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?

Two sentences with no filler, front-loaded with the core purpose and key details. Efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, output mapping to ANSWER categories, and fallback behavior. Return format is implicit but clear from 'Generate questions'. Slightly light on what the exact output looks like, but acceptable given no output schema.

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 already documents all parameters (100% coverage). Description adds meaning by explaining when brandName and description are needed (when the site cannot be read), going 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?

Clearly states the tool generates AI-visibility questions for any brand from its website, with a specific verb and resource. It maps to six ANSWER categories, distinguishing it from sibling content-retrieval tools.

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?

Mentions usefulness for AEO/GEO research and content planning, providing clear context. It does not explicitly exclude alternatives or name sibling tools, but the generative nature is distinct enough.

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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