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competitive_trends

Read-only

Time series (day/week/month/quarter) of ONE competitive metric vs your tracked competitors within a segment, over a date range — the QWA "Competitive Trends" chart. Covers market share, average rank, AI Overviews on-SERP market share, featured snippets, brand mentions, ranked keywords/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 + each competitor per time bucket. Segment-level 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_trends"
  }
}

Input Schema

NameRequiredDescriptionDefault
vsNoOptionally ALSO plot one of YOUR metrics — clicks, impressions, CTR or average position — on a right axis as a parallel series, joined to the competitive metric on the same time buckets (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.
pageNoNOT supported in v1 — page drill is rejected (segment-level only).
brandNoRestrict to branded ("Yes") or non-branded ("No") demand
grainNoGranularity — only segment-level is supported in v1; keyword/page are rejected.
top_nNoMax competitors to read
deviceNoFilter by device, e.g. "MOBILE" or "DESKTOP"
intentNoRestrict to ONE search intent (a customer keyword group)
mediumNocompetitor source medium, e.g. "google/organic" (default) or "google/cpc"
metricYesWhich competitive metric family to trend vs competitors: market_share, avg_rank, featured_snippets, ai_overviews (Google AI Overviews inclusion), brand_mentions, ranked_keywords, ranked_pages.
countryNoFilter by country code, e.g. "usa"
metricsNoOpt-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.
segmentNoTracked segment ID; omit to use the primary "default" (Overall Market) segment.
annotateNoWhen true, attach deterministic insight annotations (trend slope + significance, latest-bucket anomaly) for YOUR series (the primary sub-metric). Default false — output is byte-identical to the unannotated response.
categoryNoNOT supported in v1 — category drill is rejected (segment-level only).
date_rangeYesExplicit 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.
granularityNoTime bucket size for the series: day, week (default), month or quarter.week

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, yet the description adds real behavioral context: rejected params (page/category/grain keyword-page rejected), the whole-domain-only constraint on the vs join, the default-segment fallback, and that annotate=false yields byte-identical output. This is well beyond what the annotations convey.

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?

It is a single dense paragraph, but it is front-loaded with the core purpose and every clause carries information (metric families, scope, default segment, metric disambiguation). Slightly long and could be split for scanning, but there is little waste.

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 complex 16-param tool with no output schema, the description covers scope, granularity, metric families, defaults, exclusions, and the return shape ('You + each competitor per time bucket'). The main under-covered area is the response structure (e.g., series/axis layout with the vs join), though 'right axis as a parallel series' partially addresses it.

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 description coverage is 100%, so the baseline is 3, but the description adds genuine semantic disambiguation (ai_overviews = on-SERP Google AI Overviews, not LLM citations) and clarifies default vs. explicit segment scoping. It layers useful meaning on top of the schema rather than merely restating it.

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+resource with scope: 'Time series (day/week/month/quarter) of ONE competitive metric vs your tracked competitors within a segment, over a date range.' It also enumerates the metric families covered and explicitly disambiguates its ai_overviews metric from the ai_visibility_* sibling tools, so the agent can tell it apart without opening the schema.

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?

Explicitly names the alternative and its condition ('NOT ChatGPT/Perplexity/Gemini citations — use ai_visibility_* for those') and states the exclusions clearly ('Segment-level only — no keyword/page/category drill in v1'). It also documents the default segment behavior, so when-to-use and when-not-to-use are both covered.

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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