CueScout

Check whether an AI engine recommends a brand

check_ai_visibility

Read-only

Use this when someone asks whether an AI answer engine names or cites a brand for a buying question, or which other brands it names. Sends one question to one search-grounded engine through its API and returns whether the brand was named, whether its domain was cited, which of the listed competitors were named, and the pages the answer cited. Each call is a fresh API request with no account history or personalization, and one question is one datapoint, not an overall visibility score.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "brand": "<brand>",
      "question": "<question>"
    },
    "name": "check_ai_visibility"
  }
}

Input Schema

Name	Required	Description
`brand`	Yes	The brand or product name to look for in the answer.
`domain`	No	The brand's domain. Supplying it enables citation detection, not just name matching.
`engine`	No	Which engine answers: perplexity (the default) or chatgpt.
`question`	Yes	A question a buyer would actually type, e.g. "best tool for tracking AI search visibility". Not a keyword: the engines answer questions.
`competitors`	No	Up to 3 competitor names to check for in the same answer.

Output Schema

Name	Required	Description	Default
`rank`	Yes
`brand`	Yes
`cited`	Yes
`engine`	Yes
`snippet`	Yes
`sources`	Yes
`question`	Yes
`mentioned`	Yes
`sentiment`	Yes
`competitors`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 14, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnlyHint/openWorldHint, but the description adds real behavioral context: each call is a fresh API request with no account history or personalization, and one question counts as a single datapoint rather than a score. That is exactly the kind of non-obvious trait annotations cannot convey.

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

Conciseness4/5

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

Three sentences, front-loaded with the usage trigger before mechanism and caveats, with no filler. Slightly dense but every clause carries information; not padded.

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?

With an output schema present, the description need not enumerate return fields, yet it summarizes them (named brand, cited domain, competitors, cited pages) and closes the loop on scope. Nothing an agent needs to invoke it correctly is missing.

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

Parameters3/5

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

Schema description coverage is 100%, so all five parameters are already documented in the schema. The description clarifies that the call targets a single engine and a single question, but adds no syntax or format detail beyond what the schema provides; baseline 3 is appropriate.

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?

States a specific verb+resource: sends one question to one search-grounded engine and reports whether a brand was named/cited and which competitors appeared. It also distinguishes itself from the sibling aggregate tool by stating the result is 'one datapoint, not an overall visibility score.'

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?

Opens with an explicit trigger ('Use this when someone asks whether an AI answer engine names or cites a brand for a buying question'), and the closing clause implicitly routes aggregate-scoring requests elsewhere. An agent knows exactly when to pick this over get_ai_visibility.

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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