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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds significant behavioral context: the tool is re-measured weekly (content stable between releases), requires no key/account, has no rate limit, and returns rules rather than figures. These details go beyond the structured annotations and help the agent understand side effects, availability, and update semantics.

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 description is longer than the high-calibration example but is well-structured and front-loaded. Each sentence serves a purpose: purpose, when-to-use, alternatives, return type, stability, licensing, and citation requirement. Some minor redundancy (e.g., 'Re-measured weekly' and 'same call returns same figures') but overall economical.

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 has no parameters and a rich output schema, the description fully compensates by explaining what the response contains (rules, no brand), the underlying data source (public archive), update frequency (weekly), and usage constraints (CC BY 4.0, no rate limit). This is complete for an agent to decide when and how to invoke it.

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, so the empty schema is fully covered. Per the rubric, a baseline of 4 is appropriate. The description does not need to explain parameter semantics, but it does clarify the output semantics (rules vs figures), which is valuable.

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 clearly states the tool's function: it returns the methodology/rules behind every figure. It explicitly says 'It returns rules, not figures' and distinguishes itself as 'the only tool that tells you how much of a difference is meaningful,' setting it apart from sibling tools that return actual visibility figures.

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 when-to-use guidance: 'Call this before quoting a number as evidence, before comparing two releases, or whenever a user asks how the measurement was made or who publishes it.' It also specifies alternatives: 'For figures use get_visibility_index or get_brand_visibility; for the series, get_history.' This is model examples of usage guidance.

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