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sonar

by app.trysonar

Generate Review Insights (AI)

sonar_generate_review_insights

WRITE tool — generates a fresh AI review analysis for a tracked app from its recent reviews (praise/complaint themes, sentiment, feature requests, trend vs the previous run). At most one analysis per app+country every 90 days (429 with the next available time while in cooldown — use sonar_review_insights to read the current one); needs at least 5 recent reviews. Requires a paid (non-trial) Indie plan and an API key with the write scope.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
app_idYesSonar app UUID of a tracked app — your own or a competitor (an `id` from sonar_list_apps). NOT a store id.
countryNoReviews market (ISO country code). Insights are generated per country. Default "us".us

TDQS

A4.7/5.0
Behavior5/5

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

While annotations already indicate a non-read, non-idempotent write operation, the description adds important behavioral details: cooldown enforcement, error status 429 with next available time, minimum review count, and plan/scope requirements. It also labels the tool as 'WRITE tool' at the start, reinforcing its mutating nature.

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 information-dense but efficient, front-loading the tool's purpose and write classification. Every clause adds necessary operational detail (cooldown, minimum reviews, auth requirements, alternative read tool) without fluff or repetition.

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?

For a write/generation tool with no output schema, the description explains what the tool produces, under what conditions it can run, its rate limits, error behavior, and prerequisite access requirements. The mention of alternative read tool also completes the workflow context.

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 explains app_id and country clearly. The description adds the behavioral context that insights are per app+country and cooldown applies to that combination, but it does not substantially add new parameter meaning beyond the schema.

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 tool's action: it generates a fresh AI review analysis for a tracked app from recent reviews, listing concrete output themes. It also distinguishes itself from the sibling read tool sonar_review_insights by explicitly saying the current analysis should be read there.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage context: one analysis per app+country every 90 days, 429 cooldown behavior, minimum 5 recent reviews required, and paid-plan/write-scope prerequisites. It explicitly directs users to sonar_review_insights when they need to read the current analysis, making the alternative clear.

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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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