CueScout

Show AI visibility for a tracked brand

get_ai_visibility

Read-only

Use this when someone asks how visible their brand is in AI answers, what their share of AI voice is, or how they compare with competitors. Reads the results CueScout already collected for the brand: how often AI answers named it, its share of voice, how often its site was cited, and the same for each competitor, over the last 7, 28 or 90 days. It does not run new checks.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_ai_visibility"
  }
}

Input Schema

Name	Required	Description	Default
`days`	No	The window to report on, in days. Defaults to 28.
`brand`	No	Which brand, by name or id, when the account tracks more than one. Leave empty to use the only brand; list_brands shows them all.

Output Schema

Name	Required	Description	Default
`days`	Yes
`brand`	Yes
`answers`	Yes
`app_url`	Yes
`engines`	Yes
`brand_id`	Yes
`rankings`	Yes
`questions`	Yes
`low_sample`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 14, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already establish readOnly/non-destructive/closed-world, so the bar is lower, yet the description adds real behavior: it reads pre-collected results rather than triggering fresh checks, and it reports over fixed 7/28/90-day windows. It does not discuss data freshness or what happens when no data has been collected yet.

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 use case and followed by scope and the negative constraint; every sentence carries information. The metric enumeration is slightly list-heavy but each item is a distinct reported value, so little is wasted.

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?

With an output schema present, the description need not explain return shape, and annotations cover safety; what remains is the usage trigger and the read-vs-run distinction, both present. The only gap is not naming check_ai_visibility for the case where the user actually wants a new check run.

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 both parameters (days enum and brand name-or-id) are already fully documented, including the 28-day default and the list_brands fallback. The description restates the 7/28/90 windows but adds no syntax or format meaning beyond the schema, so the baseline of 3 applies.

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 ('Reads the results CueScout already collected for the brand') and enumerates the concrete outputs: naming frequency, share of voice, site citation rate, per competitor. The closing line 'It does not run new checks' cleanly separates it from the sibling check_ai_visibility.

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?

It gives explicit trigger phrases ('when someone asks how visible their brand is in AI answers, what their share of AI voice is, or how they compare with competitors'), which is strong positive guidance. The negative case is only implied by 'does not run new checks'; the alternative tool is not named outright, so it stops short of a full when-not routing statement.

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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