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sonar

by app.trysonar

Keyword Suggestions

sonar_keyword_suggestions
Read-only

Get autocomplete suggestions for a seed keyword from the App Store or Google Play. Returns terms with a priority score (higher = more searched). Lighter and faster than sonar_keyword_search — use when you only need term ideas without difficulty/popularity scoring. Works without an API key (free tier, limited daily use per IP).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedYesSeed keyword. The store will return autocomplete suggestions starting from this term.
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

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already provide readOnlyHint=true, so the description need not reiterate non-destructiveness. It adds useful context: the tool is lighter/faster, works on free tier with daily limits per IP, and returns a priority score. A minor gap is not specifying the return format (e.g., list of terms with scores) since there is no output schema.

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?

Three short, information-dense sentences with no fluff. Every sentence adds value: core action, differentiation from sibling, and usage constraint (free tier). Front-loaded with the main purpose.

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 tool's simplicity (3 params, no nested objects, no output schema), the description covers the core semantics, differentiation, and API key constraint. A minor gap is not describing the return format or the priority score range, but overall it's sufficient for an agent to decide and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds value beyond schema by explaining what the output (priority score) means and the overall purpose of suggestions (autocomplete), justifying a 4.

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 uses a specific verb+resource ('Get autocomplete suggestions for a seed keyword') and explicitly differentiates from siblings ('Lighter and faster than sonar_keyword_search') and specifies the stores it covers (App Store, Google Play).

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?

The description explicitly states when to use this tool vs sonar_keyword_search ('Lighter and faster... use when you only need term ideas without difficulty/popularity scoring'), and mentions it works without an API key (free tier, limited daily use per IP), providing an important constraint.

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