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

Annotations already cover read-only, idempotent and non-destructive safety, and the description adds real behavioral context on top: no account needed, evidence includes coverage and dates, ambiguous identity returns candidate choices rather than an error, and 'not indexed does not mean no AI visibility' pre-empts a false negative reading. It also discloses the one-cited-page limit when supported.

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 purpose and the free/no-account qualifier are front-loaded, and every sentence carries distinct information. It is dense to the point of semicolon-heavy packing in the third sentence, but nothing is redundant 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?

Even though an output schema exists, the description covers the practically important return cases — evidence with coverage/dates, ambiguous-identity candidates, and the single cited page — plus the expansion path. Combined with annotations and a fully documented single parameter, an agent has everything needed to call it and interpret a null result.

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 coverage is 100% and the single parameter is already documented as a name, website or returned slug, so the baseline is 3. The description adds the ambiguity behavior (candidate choices returned) and the slug-based handoff to parse_get_brand, which gives the 'slug' input real operational meaning.

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 verb and resource: an AI visibility lookup for a brand, company, product or competitor, by name or website. It also names the sibling it is not — parse_get_brand is the expansion path using a returned slug — so an agent can distinguish the two without opening either schema.

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?

Explicitly scopes usage to 'AI mentions, recommendations and discoverability research without an account' and routes the agent to parse_get_brand with a returned slug for deeper data. It stops short of stating when not to use it (e.g., when a brand is already tracked), but the entry/expansion split is clear.

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