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

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, but the description goes further by revealing the external API mechanism (Gemini with Google Search grounding, 'a real, cited web search'), the cost model ('billed per query'), and the rate limit ('a few calls per day per user'), which are critical behavioral traits not captured in the annotations.

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?

The three-sentence description is dense but well-structured: the first sentence front-loads the core purpose, the second details the output contract, and the third covers constraints. It is slightly long with embedded clauses but remains free of redundancy and filler.

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?

Given the tool's complexity—external API call, multiple output categories, cost and rate limits—the description covers the mechanism, the five-rung output ladder, additional returned data (shortlist names, source domains), and constraints, leaving no critical gap for an agent to call it correctly.

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?

The input schema covers all three parameters (brand, domain, prompt) with descriptions and concrete examples, achieving 100% schema description coverage, so the baseline holds. The description adds minor context ('buyer-shaped prompt' clarifies the prompt parameter) but does not substantially expand parameter meaning beyond what the schema already provides.

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?

The description states a specific action ('Run one buyer-shaped prompt through Gemini with Google Search grounding') and a specific report output ('where the brand lands on the five-rung ladder'), enumerating all five rungs (absent, cited, mentioned, recommended, recommended against). It also clearly differentiates this from sibling tools (growthr_fix_order, growthr_llms_txt, growthr_scan) by focusing on AI answer visibility rather than order fixing, LLM.txt files, or scanning.

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?

The description provides explicit usage constraints ('One prompt per call', 'limited to a few calls per day per user', 'billed per query') that tell the agent when to be economical, and the phrase 'buyer-shaped prompt' clarifies the intent of the input. However, it does not explicitly state when to prefer this tool over a sibling or when not to use it (e.g., if a simple scan suffices).

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