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App Store Review Intelligence MCP Server

Compare apps side by side

compare_apps
Read-onlyIdempotent

Benchmark 2-10 Apple or Google Play apps in one storefront to compare ratings, price, versions, and recent-review sentiment, with per-metric leaders.

Instructions

Benchmark 2-10 apps in one storefront for competitor analysis: store rating, rating count, price, current version & release date (Google adds installs), plus recent-review metrics (sample average, % negative / positive, latest-version average, top 5 complaints) and per-metric leaders. Apple ids and Google Play package names can be mixed (e.g. iOS vs Android of the same app). Examples: "Notion vs Evernote", "which meditation app has the happiest users". One flat price per call. Get ids from search_apps first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoGoogle Play only: review/listing language, e.g. "en", "de", "pt-BR". Default "en". Ignored for Apple.en
pagesNoReview pages to fetch, ~50 newest reviews each (1-10). Default 2.
app_idsYes2-10 app ids, e.g. ["284882215","389801252"] or ["389801252","com.instagram.android"].
countryNo2-letter storefront country code, e.g. "us", "gb", "de". Default "us".us

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive and open-world, so the safety profile is covered. The description adds genuinely new operational context: cost ("One flat price per call"), cross-storefront id mixing, and that Google adds installs data while Apple does not.

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 core verb and scope, then packs metrics, id-mixing rules, examples and the dependency note into a dense block. Nothing is redundant, though the middle enumeration of every returned metric makes the sentence heavy.

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?

There is no output schema, and the description compensates by listing the exact comparative fields returned (ratings, price, version/release date, review aggregates, top complaints, per-metric leaders). Combined with the cost note and id-sourcing prerequisite, an agent has everything needed to call it correctly.

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 the schema already documents app_ids formats, pages, lang and country. The description reinforces the 2-10 bound and the Apple/Google mixing rule but adds no syntax or constraints beyond what the schema states; baseline 3 applies.

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 ("Benchmark 2-10 apps in one storefront") and enumerates exactly what is compared, making it clearly distinct from search_apps (discovery) and get_app_reviews (single-app reviews). An agent can tell it apart from siblings without opening any schema.

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 the purpose (competitor analysis), example natural-language queries, and a concrete prerequisite: "Get ids from search_apps first." It does not explicitly say when to prefer review_insights or get_app_reviews over this tool, so it stops just short of full when/when-not guidance.

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