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Mencoro

Biggest tracked-query movers

get_query_movers
Read-onlyIdempotent

Ranks a project's tracked queries by how much a metric changed between the given window and the immediately preceding window of equal length — the biggest gainers and losers. Each row is one tracked query (a single engine + country) with its current position/share/sentiment and the signed trend delta (positive = improved). Sort by one of the trend keys; sortOrder desc = top gainers, asc = top losers. Dates must fall within the data retention window. Answers questions like "which queries moved the most" or "my biggest gains and drops versus last period".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
dateToYes
offsetNo
sortByNowhich trend delta to rank bytrend_share_of_voice
enginesNoallowed values: chatgpt, claude, perplexity, google_ai_overview, google_ai_mode, google_serp, google_shopping
dateFromYes
countriesNoISO-3166 alpha-2 country codes (e.g. "US", "GB", "DE"); a project's configured codes are listed by get_available_filters
projectIdYes
sortOrderNodesc for top gainers, asc for top losersdesc
organizationIdYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYes
totalYes
dataDirtySinceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / engines / description
      Previous value: -"allowed values: chatgpt, perplexity, google_ai_overview, google_ai_mode, google_serp, google_shopping"New value: +"allowed values: chatgpt, claude, perplexity, google_ai_overview, google_ai_mode, google_serp, google_shopping"
    • changedInput schema / properties / engines / items / enum
      Previous value: -[
      -  "chatgpt",
      -  "perplexity",
      -  "google_ai_overview",
      -  "google_ai_mode",
      -  "google_serp",
      -  "google_shopping"
      -]New value: +[
      +  "chatgpt",
      +  "claude",
      +  "perplexity",
      +  "google_ai_overview",
      +  "google_ai_mode",
      +  "google_serp",
      +  "google_shopping"
      +]
  2. Changed2 schema fields changed
    • addedOutput schema / properties / dataDirtySince / anyOf
      Added value: +[
      +  {
      +    "type": "string"
      +  },
      +  {
      +    "type": "null"
      +  }
      +]
    • removedOutput schema / properties / dataDirtySince / type
      Removed value: -[
      -  "string",
      -  "null"
      -]
  3. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already establish the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds genuinely useful behavior: the delta is computed against the immediately preceding equal-length window, dates must fall within the data retention window, and each row carries current position/share/sentiment plus a signed delta. It does not mention pagination behavior or result size caps, which is the only notable gap.

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 ranking definition before the row shape and sort mechanics, and each sentence adds information (comparison window, row granularity, sort semantics, retention constraint, example questions). It runs long and slightly restates the gainers/losers idea, but there is no filler sentence.

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 10-parameter read tool with an output schema and full annotation coverage, the description supplies the comparison mechanics, sort semantics, retention constraint, and question routing — enough to call it correctly. The residual gap is undocumented pagination identifiers (limit/offset), which the schema defaults partly cover.

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 only 40% (10 params), so the description carries more burden than usual. It does explain the two tricky params well — sortBy picks which trend delta to rank by, and sortOrder desc = gainers / asc = losers — and clarifies that dates are bounded by retention. But limit, offset, organizationId, projectId, and the date params receive no semantics anywhere, leaving a real coverage 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 and resource — ranks a project's tracked queries by metric change between adjacent equal-length windows — and defines the row granularity (one engine + country per tracked query). An agent can distinguish this from the sibling time-series tools (get_tracked_query_time_series, get_rank_tracking_time_series) because it is a cross-window comparison, not a series. The sign convention (positive = improved) removes ambiguity about what a 'mover' is.

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 triggering context with example questions ('which queries moved the most', 'my biggest gains and drops versus last period'), which tells the agent when this tool fits. It does not, however, explicitly name an alternative or state when not to use it versus the time-series or search_tracked_queries siblings. Clear usage context without exclusions.

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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