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sonar

by app.trysonar
OverviewDiscussionsAdmin

Fetch App Reviews

sonar_app_reviews
Read-onlyIdempotent

Fetch user reviews for an app. Supports filtering by star rating range and sorting by recent or helpful. Useful for sentiment analysis, feature-request mining, and competitive research.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoAndroid only: fetch one language feed (e.g. ar, fr). Omit to merge the market language plus en, es, fr, ar; coverage is not exhaustive.
sortNoSort order. "recent" returns newest first, "helpful" returns most-voted first.recent
limitNoMaximum number of reviews to return (1-200).
storeYesApp store. "ios" for Apple App Store, "android" for Google Play.
countryNoISO 3166-1 alpha-2 country code (e.g. "us", "gb", "de"). Default "us".us
store_idYesStore-specific app identifier. iOS: numeric track ID. Android: package name.
max_ratingNoFilter to reviews with a star rating <= this value (1-5).
min_ratingNoFilter to reviews with a star rating >= this value (1-5).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / lang
      Added value: +{
+  "description": "Android only: fetch one language feed (e.g. ar, fr). Omit to merge the market language plus en, es, fr, ar; coverage is not exhaustive.",
+  "pattern": "^[a-z]{2}$",
+  "type": "string"
+}
  2. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds filtering and sorting capabilities and use-case context, but does not disclose behavioral traits like pagination, rate limits, auth requirements, or return format.

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?

Two tightly written sentences with the primary action front-loaded; no filler or redundancy. The second sentence efficiently summarizes supported filters and intended uses.

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 an eight-parameter fetch tool with full schema coverage and safety annotations, the description is largely complete. It covers what the tool does and its filtering options, though it does not describe the shape of returned review data (e.g., fields, pagination) despite having no output schema.

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 fully documents all eight parameters including the rating-range filters and sort options. The description merely restates the filtering and sorting features without adding syntax, format, or edge-case meaning beyond the schema.

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 ('Fetch') and resource ('user reviews for an app'), making the basic purpose clear. However, it does not distinguish this raw-fetch tool from sibling tools like sonar_review_insights or sonar_generate_review_insights, which likely provide derived analysis rather than raw 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 description lists use cases (sentiment analysis, feature-request mining, competitive research) but gives no explicit when-to-use guidance, prerequisites, or alternatives to this tool. Usage is implied rather than directed.

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

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