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QQQ AI Visibility Checker

Get the public AI visibility report of a domain

get_public_report
Read-onlyIdempotent

Returns the latest public AI visibility report of a domain from QQQ (qqq.ee), when someone has already run the full check on qqq.ee: report date, market and field, in how many search-grounded ChatGPT, Gemini, Perplexity and Claude answers the company was named, how often its own website was cited, a summary per AI engine, the questions asked, the cited source domains and short answer excerpts, plus the link to the report page. Reads the stored report only: no website is fetched, no AI model is asked and no new report is created. Without a report, says so and links the full check on qqq.ee. Read-only, no limit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesWebsite domain or URL, for example example.com or https://www.example.com/.
languageNoLanguage of the answer and of the qqq.ee links: en (English, default) or et (Estonian).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Goes beyond the annotations by stating that no website is fetched, no AI model is queried, and no report is created, plus the no-report behavior and 'no limit'. The 'Read-only' phrasing partly restates readOnlyHint=true, but the negative-scope disclosure is genuinely additive.

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?

Purpose is front-loaded and the long enumeration of returned fields is informative rather than padding. The single dense paragraph is slightly list-heavy but every sentence carries content.

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 no output schema, the description compensates by enumerating the returned payload (report date, market/field, engine coverage, citations, questions, excerpts, report link). An agent knows both what it gets back and the no-report case.

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 the domain format and the en/et language enum are already documented in the schema. The description adds no syntax or default guidance beyond that, so the baseline 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?

States a specific verb and resource ('Returns the latest public AI visibility report of a domain') and immediately scopes it to stored reports only. It is clearly separable from the sibling check_ai_visibility, which it implicitly contrasts with by noting that no new report is created.

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?

Gives a clear precondition ('when someone has already run the full check on qqq.ee') and describes the fallback when no report exists. It does not name check_ai_visibility as the tool to use to run the check, leaving the sibling routing slightly implicit.

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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