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Glama

sonar

by app.trysonar

Fetch App Reviews

sonar_app_reviews
Read-only

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
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).

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so this is a non-destructive read operation. The description does not contradict this and adds transparent context about the data being reviews (textual content with ratings). However, it does not disclose potential pagination behavior, rate limits, or whether the data is live vs cached, so some behavioral gaps remain.

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?

The description is extremely concise at just two sentences. The first sentence defines the core action and the second sentence lists clear, high-level use cases. Every word earns its place with zero redundancy.

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?

Given the full schema (100% description coverage, enums, 7 well-documented params) and the readOnlyHint annotation, the description is largely complete for a list/fetch tool. It could optionally mention what the return value looks like, but since no output schema is defined, a minor expectation gap exists. Still, for its complexity level, it is thorough.

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 7 parameters are already documented in the input schema with descriptions and constraints (enums, min/max, defaults). The description adds marginal value beyond the schema by tying the parameters to the tool's purpose (filtering by star rating, sorting), but does not provide new semantic context. Baseline score of 3 is appropriate.

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?

The description clearly states the action ('Fetch user reviews'), the target resource ('for an app'), and the key capabilities (filtering by star rating, sorting). It also provides use cases (sentiment analysis, feature-request mining, competitive research), which strongly differentiates it from siblings like sonar_app_changes or sonar_app_keywords.

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 description gives clear context for when to use the tool (sentiment analysis, feature-request mining, competitive research). However, it does not explicitly specify when NOT to use it or mention alternatives among the many sibling tools.

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

All sonar toolsInspect
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TDQS

A3.9/5.0
Disambiguation5/5

Each tool targets a distinct action or data aspect within the ASO domain. Keyword-related tools are clearly separated by purpose (tracked keywords vs. research vs. suggestions vs. metrics), and competitive tools differentiate between reading landscape and generating new analysis. No two tools have overlapping functionality that would confuse an agent.

Naming Consistency3/5

Naming is a mix of verb-first (e.g., sonar_add_screenshot, sonar_create_product) and noun-first patterns (e.g., sonar_app_keywords, sonar_competitor_landscape). While all use snake_case and the 'sonar_' prefix is consistent, the lack of a uniform verb_noun structure makes it harder to predict tool names. The pattern is readable but inconsistent.

Tool Count2/5

With 47 tools, the server is over-scoped for a typical MCP server. Although the ASO domain is broad, many tools are granular (e.g., 10 screenshot tools, 10 keyword tools). This quantity exceeds the 25+ threshold for 'too many' as defined in the calibration, making it heavy for an agent to navigate comprehensively.

Completeness5/5

The tool surface covers the full ASO lifecycle: app discovery, keyword research, tracking and ranking, competitor analysis, revenue estimation, screenshot creation and export, alerts, and product management. There are no obvious gaps—every necessary operation for monitoring and optimizing app store presence is present, including both read and write actions.

Resources