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EuroScrape

EuroScrape MCP server

Official

App Store Reviews Scraper + Google Play Reviews (All Countries)

get_app_reviews
Read-only

Fetch App Store and Google Play reviews for chosen apps and countries, including ratings, text, versions, dates, replies, and summary by version and country.

Instructions

App Store and Google Play reviews of an app in the countries you choose: rating, text, version, date, developer reply, and a summary by version and country. Pay per result on your Apify account: run start $0.003, review delivered $0.0002.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
appsNoApp Store URLs or IDs (e.g. https://apps.apple.com/us/app/instagram/id389801252 or id389801252) and Google Play URLs or package names (e.g. com.spotify.music). Mix both stores freely.
sortByNoWhich reviews first.
appNamesNoJust type the app name (e.g. "Duolingo"): the Actor finds it on the App Store and on Google Play.
countriesNoReviews differ per country: each country is a separate storefront.
maxRatingNoE.g. 2 = only negative reviews.
maxReviewsPerCountryNoThe App Store gives at most 500 recent reviews per country; Google Play has no such limit. Default here: 25.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so safety is covered. The description adds genuinely new context beyond structured data: the pay-per-result billing model with specific prices (run start $0.003, review delivered $0.0002) and the aggregated summary output. Only the run-behavior (async job, pagination) is left unstated.

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 capability and returned fields, then pricing. Every clause carries information, though the title's 'All Countries' is somewhat redundant with the body.

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 six-parameter, zero-required tool with no output schema, the description covers what it returns, the two stores, and the cost model, which is enough to call it correctly. Minor gaps (async execution, run limits) remain but are non-blocking.

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 six parameters are already documented in the schema, including examples and the country enum. The description only loosely references 'the countries you choose' and 'summary by version and country,' adding no syntax or default detail beyond the schema. Baseline 3 is correct.

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+resource (fetch App Store and Google Play reviews of an app) and enumerates the returned fields (rating, text, version, date, developer reply, summary by version/country). No sibling tool covers reviews, so the agent can distinguish it instantly.

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?

Usage is implied by the purpose (use it to scrape reviews for chosen countries), but there is no explicit when-to-use vs when-not, no prerequisites, and no alternative named. Adequate but leaves selection reasoning to inference.

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