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Review counts by rating, sentiment or tag

sensortower_review_breakdown
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Aggregate app review counts by rating, sentiment, or tag to reveal star breakdowns, customer happiness, and complaint themes across dates, regions, languages, or app versions.

Instructions

Aggregate review counts. by=rating answers 'how do the stars break down', by=sentiment 'how happy are they', by=tag 'what are they complaining about'. Each accepts a plain breakdown or one prefixed with date, region, language or app_version.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
byNorating
limitNoKeep at most this many rows.
fieldsNoComma-separated allowlist of output fields. Strongly recommended: SensorTower rows are wide.
formatNoOutput encoding. csv is markedly cheaper in tokens for wide, flat results.
prefixNoSlice the breakdown further.
app_idsYes
dry_runNoPrint the URL that would be called (token redacted) and charge 0 requests.
regionsNo
end_dateYesEnd of the window, YYYY-MM-DD (inclusive).
languagesNo
start_dateYesStart of the window, YYYY-MM-DD (inclusive).
date_granularityNoRequired whenever a breakdown contains `date`.month

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered and the description does not contradict it. The description adds no behavioral context beyond the parameter semantics (no mention of request cost, pagination, or result shape), so with annotations present a 3 is appropriate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences, front-loaded with the core action, then the by-value interpretations, then the prefix mechanics. No filler, and the opening verb+resource is immediately scannable.

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?

For a 12-parameter tool with no output schema, the description resolves the two most ambiguous parameters (by, prefix) but leaves the return shape as merely "counts" and says nothing about the other parameters, which rely on 67% schema coverage. Adequate for the core decision but not complete.

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?

With schema coverage at 67%, the description still adds real meaning: it explains what `by` values answer and, crucially, the `prefix` concept ("a plain breakdown or one prefixed with date, region, language or app_version") that the schema only lists as an enum. This is meaningful value beyond the structured field.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ("Aggregate review counts") and clarifies what each `by` value delivers, which is more than a name restatement. It does not, however, differentiate itself from the adjacent siblings sensortower_reviews and sensortower_ratings, so the agent must infer that this is the aggregated/breakdown variant.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The mapping of by=rating/sentiment/tag to plain-English questions ("how do the stars break down", "how happy are they") is genuinely useful selection guidance for the key enum. But there is no explicit when-to-use-this-vs-alternatives and no exclusions, so it remains implied usage rather than routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.