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Keyword Research

sonar_keyword_search
Read-only

Research a keyword and related terms. Returns difficulty (0-100), popularity score, and results count for the seed keyword plus related autocomplete suggestions. Use this to find keywords worth targeting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSeed keyword to research (e.g. "meditation", "recipe app").
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

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the description correctly implies no destructive behavior. It adds value by specifying the exact returned fields (difficulty, popularity, results count, autocomplete suggestions). However, it does not disclose any rate limits, authentication requirements, or potential side effects beyond what annotations provide, so a 3 is appropriate.

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 concise sentences with no wasted words. The first sentence states the purpose and output, the second gives usage guidance. Every sentence earns its place.

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?

With no output schema, the description adequately covers what is returned (difficulty, popularity, results count, autocomplete suggestions). It also explains the seed keyword concept. For a tool with three well-documented parameters, this is sufficient. A minor gap is not specifying the number of autocomplete suggestions or whether pagination exists, but overall it is complete enough.

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% because all three parameters have descriptions. The description adds no new parameter-specific information beyond what the schema provides; it only uses the term 'seed keyword' which matches the 'query' parameter. 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?

The description specifies the verb 'research' and the resource 'a keyword and related terms', and lists the return values (difficulty, popularity, results count, autocomplete suggestions). This distinguishes it from sibling tools like sonar_keyword_metrics (which likely returns only metrics) and sonar_keyword_suggestions (which may return only suggestions).

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 includes 'Use this to find keywords worth targeting.' which gives clear context for when to invoke the tool. However, it does not explicitly mention when NOT to use it or alternatives among the many sibling tools, so it falls short of a 5.

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.