Quattr

ai_citation_attribution

Read-only

AI answer-engine (ChatGPT/Perplexity/Gemini/AI Mode) citations — NOT Google's on-SERP AI Overviews (use competitive_trends for those). Which prompts cite our pages in AI answers, and how much? Ranks prompts (keyword) by how many of your pages are referenced in the AI answer — so you see where you earn AI citations. Use for "which prompts cite us most", "AI citation attribution", "where do we win AI answers". Per-segment.

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": {
      "segment": "<segment>",
      "date_range": "<date_range>"
    },
    "name": "ai_citation_attribution"
  }
}

Input Schema

Name	Required	Description
`brand`	No
`top_n`	No
`intent`	No
`segment`	Yes	An ANSWER-ENGINE scope: an AI-visibility tracker id (decomposed to its per-engine segments) or one per-engine segment id. A conventional organic/paid search segment holds no AI-answer citation data and is declined — use market_share_breakdown or gsc_breakdown for that grain. resolve_entity first if unsure.
`platform`	No	Answer engine to scope to ("chatgpt"/"gemini"/"aimode"/"perplexity") — picks that engine's market-share segment when `segment` is a tracker. Omit for all engines.
`date_range`	Yes	Explicit date range, e.g. "2026-05-01 to 2026-05-31". Both endpoints are INCLUSIVE — the END day is covered (a 7-day range = 7 days). Quattr weeks run Sunday–Saturday; for "last week" use the most recent complete Sun–Sat span.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 19, 2026

TDQS

A4/5.0

Behavior3/5

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

Annotations already declare readOnly/openWorld/destructive hints, so the safety profile is covered. The description adds domain-scoping context (answer-engine citations vs. SERP AI Overviews) but does not disclose return format, engine availability, or any rate/auth behavior. Adequate but not rich given annotations carry the main load.

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?

Front-loads the crucial disambiguation (answer engines vs. AI Overviews) in the first clause, then the purpose and use-cases. Slightly dense with quoted intent phrases, but every sentence adds routing value.

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

Completeness3/5

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

With no output schema, the description should clarify the return shape; it does state the result is prompts ranked by page-citation count, which is useful, but leaves the ranking metric and unlisted parameters unexplained. Adequate for routing but incomplete for a 6-parameter tool.

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 coverage is only 50% (segment/platform/date_range documented, but brand, top_n and intent are undocumented). The description's 'ranks prompts by how many pages are referenced' implies a ranked/top-N behavior but adds no syntax or meaning for the undescribed parameters, so it only partially compensates for the gap.

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 (ranks) and resource (prompts/keywords) with the exact output grain ('by how many of your pages are referenced in the AI answer'). It explicitly distinguishes itself from a named sibling (competitive_trends for Google AI Overviews) and from AI Overviews generally, so an agent can route without opening schemas.

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?

Gives explicit when-to-use phrasing (quoted user intents like 'which prompts cite us most') and an explicit when-not ('NOT Google's on-SERP AI Overviews — use competitive_trends'). The condition selecting the alternative is spelled out, leaving nothing to inference.

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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