Quattr

competitive_overview

Read-only

Snapshot of ONE competitive metric vs your tracked competitors within a segment: AI Overviews on-SERP market share, featured snippets, brand mentions, ranked keywords, or ranked pages. Its ai_overviews metric is Google AI Overviews inclusion (the on-SERP answer box) — NOT ChatGPT/Perplexity/Gemini citations (use ai_visibility_* for those). You vs each competitor for the chosen metric, optionally period-over-period. Segment-level snapshots only (no keyword/page/category drill in v1). Defaults to the primary 'default' (Overall Market) segment when omitted — name another tracked segment to scope to it.

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": {
      "metric": "<metric>",
      "date_range": "<date_range>"
    },
    "name": "competitive_overview"
  }
}

Input Schema

Name	Required	Description
`vs`	No	Optionally ALSO plot one of YOUR metrics — clicks, impressions, CTR or average position — on a right axis, joined to the competitive metric on the same period (like the QWA "Market Share v/s Clicks" chart). Whole-domain only: can be joined ONLY on the default segment (which spans the whole domain). Omit for the competitive metric alone.
`page`	No	NOT supported in v1 — page drill is rejected (segment-level snapshots only).
`brand`	No	Restrict to branded ("Yes") or non-branded ("No") demand
`grain`	No	Granularity — only segment-level is supported in v1; keyword/page are rejected.
`top_n`	No	Max competitors to read for long-domain families
`device`	No	Filter by device, e.g. "MOBILE" or "DESKTOP"
`intent`	No	Restrict to ONE search intent (a customer keyword group)
`medium`	No	competitor source medium, e.g. "google/organic" (default) or "google/cpc"
`metric`	Yes	Which competitive metric family to snapshot vs competitors: featured_snippets, ai_overviews (Google AI Overviews inclusion), brand_mentions, ranked_keywords, ranked_pages.
`country`	No	Filter by country code, e.g. "usa"
`metrics`	No	Opt-in sub-metric keys within the family (e.g. ["negative_mentions"] for brand_mentions). Defaults to the family's default sub-metrics. Unknown keys are rejected with the valid list.
`segment`	No	Tracked segment ID; omit to use the primary "default" (Overall Market) segment.
`category`	No	NOT supported in v1 — category drill is rejected (segment-level snapshots only).
`competitor`	No	Optional single competitor domain/label to focus on
`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.
`comparison_range`	No	Explicit comparison range for period-over-period, e.g. "2026-04-01 to 2026-04-30"

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 declare readOnly/non-destructive/non-open-world, so the safety profile is covered. The description adds genuinely useful behavioral context beyond that: the v1 rejection of page/keyword/category drill, the fact that `vs` can only be joined on the whole-domain default segment, and the segment-defaulting behavior — constraints an agent would otherwise trip over.

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-loaded with the core capability, then the metric disambiguation, then the v1 limits and defaults — logical order, dense sentences, no filler. It is on the long side for a description, but each clause carries a distinct constraint rather than restating the schema.

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?

For a 16-parameter tool with no output schema, the description covers the metric semantics, v1 scope limits, defaulting behavior, and the `vs` join restriction, which are the highest-risk ambiguities. Minor gaps remain around the opt-in `metrics` sub-metric keys and period-over-period output shape, but the essentials for correct invocation are present.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds semantic value the schema does not: the ai_overviews meaning is disambiguated against LLM-citation metrics, and the default-segment scoping rule is stated. The remaining parameters (device, country, top_n, medium, brand) are left entirely to their schema descriptions.

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?

Opens with a specific verb+resource+scope ('Snapshot of ONE competitive metric vs your tracked competitors within a segment') and enumerates the exact metric families available. It also explicitly separates itself from the ai_visibility_* siblings by stating that ai_overviews is on-SERP AI Overviews inclusion, not LLM citations.

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 clear conditions: use ai_visibility_* for ChatGPT/Perplexity/Gemini citations, segment-level only in v1, defaults to the 'default' segment unless another tracked segment is named. It stops short of routing to the other close siblings such as competitive_trends or competitive_keyword_ranking, so it is not fully exhaustive.

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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