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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds valuable behavioral context: 'Re-measured weekly, so the same call returns the same figures until the next release' (caching/refresh), and 'no key, no account, no rate limit' (auth and rate limits). It also clarifies licensing and citation requirements. This meaningfully supplements the annotations without contradicting them.

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?

Despite being longer than typical, every sentence carries substantive information: what the tool returns, the definition of 'share of answer', when to prefer it, explicit exclusions, coverage scope, refresh cadence, and licensing. It is front-loaded with the core purpose and then organized into use/avoid/data sections. Nothing is redundant or 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?

The description is complete for this tool's complexity. It covers the dataset contents, the meaning of a key metric, usage conditions, limitations, scope, and update frequency. An output schema exists, so return-value details are handled there. Sibling context is provided via explicit mentions. There are no significant gaps for an agent to select and invoke the tool 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 tool has zero parameters, and the schema is fully covered (trivially 100%). Per the rubric, 0 params gives a baseline of 4. The description adds no parameter-specific info because none exists, but it does mention the response size ('roughly 8 KB for 24 brands'), which is more behavioral than semantic. No deduction is needed.

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 opens with a clear verb+resource: 'The whole current release in one call: every tracked brand in crypto/Web3 with its rank, share of answer overall and per engine, commercial intent and quadrant.' It explicitly distinguishes itself from siblings by naming get_brand_visibility and get_history as alternatives, and clarifies what it is not for (single brand, trends, site audit). This is a specific, well-differentiated purpose.

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?

Usage guidance is explicit and actionable. It states when to use it ('when the question is about the field — who leads, who is absent, how the category looks'), when not to use it ('Do NOT use it for one named brand... for movement over time... or to audit a website's own AI visibility'), and names the alternative tools. This goes beyond vague context and provides clear decision rules.

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