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vom-core

storelift-mcp

search_reviews

Read-onlyIdempotent

Search an app's store reviews by text, rating, version, or date range to analyze feedback and monitor user issues.

Instructions

Search one app's reviews in one storefront by text, star rating, app version and date range, newest first — up to 1,000 rows from the full review archive within your plan's review window. total is the match count; truncated tells you when more rows matched than you asked for.

Input Schema

NameRequiredDescriptionDefault
qNotext to find in the review title or body
toNolast day, YYYY-MM-DD
fromNofirst day, YYYY-MM-DD
appIdYesapp id from list_apps
limitNorows, up to 1000 (default 100)
ratingNostar ratings, comma separated, e.g. 1,2
countryNocountry code, e.g. tr, us (defaults to the app's first country)
versionNoapp version the review was written on, e.g. 2.4.0
platformNodefaults to ios

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive, so safety is covered. The description adds real operational context beyond that: a 1,000-row cap, newest-first ordering, the plan's review-window limitation, and the meaning of the `total` and `truncated` response fields — valuable since no output schema exists.

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?

Two sentences, front-loaded with the core action and filters, then the return semantics. Dense but every clause carries information; only the return-field sentence could be considered optional given it duplicates no schema (there is none), so it earns its place.

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 9-parameter filtered-read tool with no output schema, the description covers purpose, filter dimensions, result cap, ordering, plan limitation, and the two meaningful return fields (total, truncated). Only the absence of explicit sibling routing keeps it from being fully complete.

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%, so all nine parameters are already documented in the schema, making a 3 the baseline. The description reinforces the limit cap and storefront scoping but adds no syntax or format detail beyond what the schema provides for q, rating, from/to, etc.

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 precise verb (search) plus resource (one app's reviews in one storefront) and enumerates the filterable dimensions (text, star rating, app version, date range) and ordering (newest first). This clearly differentiates it from siblings like get_reviews, hive_app_reviews, and summarize_reviews.

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 scoping phrase 'one app's reviews in one storefront' implies the use case, and 'within your plan's review window' hints at a constraint, but there is no explicit when-to-use guidance or named alternative (e.g., when to use this over get_reviews or summarize_reviews). Usage is implied rather than stated.

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