Skip to main content
Glama
vom-core

storelift-mcp

get_review_stats

Read-onlyIdempotent

Track an app's review ratings by day and version to see how rating drops align with releases, including average, distribution, and 1–2 star share.

Instructions

Star-rating distribution, average and share of 1–2 star reviews for one app in one storefront, plus a day-by-day curve (count and average per day) and a per-version breakdown — so a drop lines up with the release that caused it. Defaults to the last 90 days.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNolast day, YYYY-MM-DD
daysNolast N days, up to 365 (default 90)
fromNofirst day, YYYY-MM-DD (overrides days)
appIdYesapp id from list_apps
countryNocountry code, e.g. tr, us (defaults to the app's first country)
platformNodefaults to ios

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish this as a safe, idempotent, closed-world read (readOnlyHint, idempotentHint, openWorldHint=false, destructiveHint=false), so the description only needs to add context. It does: the 90-day default window, the one-app/one-storefront scope, and an itemized list of what comes back. It stops short of noting cost, rate limits, or behavior when an app has no reviews.

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?

A single sentence, front-loaded with the return payload and closed with the rationale, plus a short default-window sentence. No filler or repetition; every clause carries information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and six parameters, the description compensates by enumerating the returned aggregates and the per-version breakdown an agent needs to interpret results. Combined with 100% schema coverage on inputs and clear read-only annotations, nothing essential is missing.

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 100%, with from/to/days/country/platform all documented including the from-overrides-days rule and the country fallback, so the baseline is 3. The description's 'one app in one storefront' and 'last 90 days' restate what the schema already says and add no new format or constraint detail.

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 specific outputs (star-rating distribution, average, 1–2 star share, day-by-day curve, per-version breakdown) for a precisely scoped resource (one app in one storefront). This clearly differentiates it from siblings like get_reviews, search_reviews and summarize_reviews, which handle individual or summarized review text rather than aggregate rating statistics.

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?

The clause 'so a drop lines up with the release that caused it' gives a concrete diagnostic use case, and 'Defaults to the last 90 days' sets the default time window. However, it never names an alternative (e.g. get_reviews for raw review text) or states exclusions, so the routing decision is implied rather than explicit.

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