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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds substantial behavioral context: market-determined fame, no per-class fame doctrine, a legal citation, the concept of using Nice class as a market proxy, and a clear caveat that this is circumstantial, not statutory proof. This goes well beyond the structured 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 description is dense but well-organized: purpose, critical nuance, return fields, use cases, and caveat. It front-loads the primary action and every sentence earns its place by addressing selection, invocation, or interpretation of results.

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?

No output schema exists, yet the description enumerates the key return fields (is_famous, famous_in_class, fame tier, market footprint, portfolio size, enforcement history), explains the market proxy, lists concrete use cases, and sets expectations with the 'circumstantial signal' caveat. This fully equips an agent to decide when to invoke and how to interpret the 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%, so baseline is 3. The description adds strong semantic value to the 'class' parameter by explaining it is a 'PROXY for that market' and why market-specificity matters, which the schema's generic 'Optional Nice class number' does not convey. The 'mark' parameter isn't enriched, but the class parameter guidance is excellent.

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 specific verb and resource ('Check whether a trademark is FAMOUS') and immediately adds the key distinguishing nuance: fame is market-specific and tested via a Nice class proxy. It also lists the returns and use cases, clearly separating it from generic lookup tools.

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 gives explicit use cases ('Use for "is X a famous trademark?"...') and a boundary ('It is a circumstantial signal, not statutory fame proof'). However, it does not name any alternative tool (e.g., get_fame_profile) or state when NOT to use this tool, so exclusion guidance is incomplete.

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