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

Beyond the readOnlyHint/openWorldHint annotations, the description discloses that the call counts toward the research quota, which is genuinely useful cost context an agent cannot get from structured fields. It also clarifies the dimensionality of the outputs (popularity per store, difficulty per store and platform). It stops short of describing result format, score ranges, or handling of unknown terms, but with annotations covering the safety profile this is adequate-to-good.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences, zero filler, with the capability and its batch limit front-loaded and the quota cost placed last where it reads as a caveat. Nothing is redundant with the title.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description is the only source of return-value information; it names the two metrics and their dimensions but gives no sense of scale, format, or what is returned for an unknown term. For a simple read-only lookup at this complexity level that is workable, but it leaves real gaps an agent would hit on first call.

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

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

Schema description coverage is 0%, so the description carries the burden for three undocumented parameters. It adds real meaning by linking parameters to outputs (store affects both metrics; platform affects only difficulty, which is an important scoping insight), and the 'up to 20' restates the schema maxItems. But it never explains the store code format (two-letter code, default 'us') or the platform enum values, so half the parameter semantics remain unstated.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource (keyword popularity and difficulty) with concrete scope: up to 20 terms, batch call, no tracking. The 'without tracking them' phrase implicitly distinguishes it from add_keywords and refresh_app_keywords, though it never names the closest sibling get_keyword_popularity, so the boundary between those two remains ambiguous.

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

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

Implied usage guidance is present — 'without tracking them' indicates this is for one-off lookups rather than ongoing keyword monitoring, and the quota warning signals a cost tradeoff. However, no sibling is named, so an agent cannot learn from this text alone whether get_keyword_popularity or get_keyword_history is the better alternative for a given request.

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