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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuine value on top: it discloses the underlying dataset (US Social Security), the year basis (2025), and explicit scope limits (no meanings/origins, US-only popularity).

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?

Front-loaded with the use case, then returns, then exclusions — a sensible order and no filler sentences. The long enumeration of filter dimensions in the first sentence is dense and partly duplicates the schema, but each clause maps to a real capability.

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?

For a 20-parameter, zero-required tool with rich annotations and an output schema, the description covers intent, data provenance, and scope limits, which is what an agent needs to call it correctly. Minor gaps remain: no guidance on how filters combine or what an empty result means, though defaults are handled in the schema.

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 all 20 parameters are already documented with examples in the schema. The description only restates the filter categories and re-explains bands like 'familiar but not common' = rank 201-1000, which the schema's popularity enum description already provides. Baseline 3 applies when the schema does the heavy lifting.

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+resource (find baby names) and enumerates the exact filter dimensions the tool supports, plus the return payload (2025 rank, births, trend) and data source (US Social Security). It never names the sibling find_name_trend, so the distinction from it is only implicit in the 'ideas or lists' framing.

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?

Opens with an explicit 'Use this when the user wants...' trigger covering all supported intents, and closes with clear when-not guidance ('does not give meanings, origins, religious or astrological criteria; do not use for non-US popularity'). It stops short of routing the agent to a named alternative tool for those excluded cases.

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