Quattr

ai_prompt_winners

Read-only

AI answer-engine (ChatGPT/Perplexity/Gemini/AI Mode) citations — NOT Google's on-SERP AI Overviews. WHO leads each AI-answer prompt, and with WHICH pages? For each prompt (pass the gap prompts from ai_prompt_coverage_gap, or omit to take the busiest prompts) returns the most-cited domains for THAT prompt — including your own domain when you lead — each with its cited URLs, citation counts, category and average position. Every relationship is keyed by the prompt: this is the supported way to say "prompt X → domain Y's page Z earns the citations". Use for "who wins the prompts we miss", "which pages beat us per prompt", "prompt-level competitor citations". 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_prompt_winners"
  }
}

Input Schema

Name	Required	Description
`brand`	No	Branded vs non-branded prompts.
`intent`	No	Restrict to one intent (resolve_entity first).
`prompts`	No	Explicit prompts to attribute (e.g. the gap prompts from ai_prompt_coverage_gap). Omit to rank the segment's busiest prompts by total citations.
`segment`	Yes	Tracked market-share segment id, OR an AI-visibility tracker id (decomposed to its per-engine segments). 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.
`top_prompts`	No	When `prompts` is omitted: how many top-citation prompts to return.
`urls_per_leader`	No
`leaders_per_prompt`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 19, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false, openWorldHint=false), so the description's added value is in describing the returned relationship shape: prompt → domain → URLs → citation counts, category, average position. It adds real behavioral context about output structure and keying, though it says nothing about pagination, ranking order, or limits.

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?

The differentiation and scope claims are front-loaded and most sentences carry weight. There is mild redundancy (the return fields are stated twice) and a dangling fragment 'Per-segment.' that adds noise without context.

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 no output schema, the description carries the burden of describing returns, and it does so adequately (domains, URLs, counts, category, average position). Combined with the workflow cross-reference and scope notes, an agent has enough to invoke it, though ordering, result limits, and behavior when `prompts` contains unknown values are unaddressed.

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?

At 78% schema coverage the baseline is 3, and the description earns above that by linking the optional `prompts` parameter to the `top_prompts` default behavior and to the ai_prompt_coverage_gap workflow. However it largely restates what the schema already documents about `segment`, `platform`, and `date_range` rather than adding depth.

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 most-cited domains per AI-answer prompt, each with cited URLs, citation counts, category, and average position. It explicitly distinguishes itself from Google's on-SERP AI Overviews and keys itself to prompt-level relationships, so an agent can separate it from generic citation 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?

Gives concrete use cases ('who wins the prompts we miss', 'which pages beat us per prompt') and a clear workflow hook: pass the gap prompts from ai_prompt_coverage_gap, or omit for busiest prompts. It does not explicitly exclude or compare against sibling attribution tools like ai_citation_attribution or ai_page_citations by name, so the when-not guidance is partial.

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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