Get personalized recommendations

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the input scope of recommendation but does not describe response shape, ranking behavior, or side effects, and there is no output schema to fill that gap.

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 a single sentence, front-loaded with the main behavior, and packs parameter semantics through compact examples. Each phrase adds meaning without raw repetition of the schema.

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 read-only, idempotent recommendation tool with a small required parameter set, this description is mostly complete: it explains the recommendation basis and optional dimensions. Without an output schema, a brief note on the returned list structure would be helpful, but 'fragrance picks' already implies the general shape.

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?

Since schema description coverage is 100%, the schema already owns formal parameter documentation. The description adds useful gloss by giving concrete values for preferences, budget units, and occasion examples, which helps an agent form realistic call payloads.

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 states a specific task: returning personalized fragrance picks based on taste preferences, budget, occasion, and gender category. It distinguishes this from the sibling search, dupe, similar, and trending tools by centering on recommendation-style filtering by personal context.

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 a clear use case and names the relevant inputs: notes, budget, occasion, and gender. It does not explicitly tell an agent when not to use this tool or name sibling alternatives, but the personalization angle makes the intended context reasonably obvious.

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