Fragrances Perfume Picks users are adding to their wardrobes most over the last 30 days (falls back to catalog popularity when live activity data is unavailable). The method used is labeled in the response.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds meaningful behavioral details: the fallback mechanism when live data is unavailable and the labeling of the method in the response, which helps the agent interpret results correctly.
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 that immediately states the purpose and key behavior. It is front-loaded and contains zero filler, conveying everything needed without waste.
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 simple tool with one optional parameter and no output schema, the description explains the data source (30-day live activity), the fallback, and that the method is labeled in the response. This fully equips an agent to use 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 schema fully covers the only parameter (limit) with a description ('Max results (default 10)'), so the tool description adds no additional semantic value beyond what the schema provides. Baseline score of 3 is appropriate given 100% schema coverage.
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 lists fragrances users are adding to wardrobes most in the last 30 days, with a fallback to catalog popularity. It distinguishes itself from sibling tools like search_fragrances or get_recommendations by focusing on trending/popular based on live activity.
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 clear context: it uses 30-day live activity data, falling back to catalog popularity when unavailable. It does not explicitly mention when not to use it or direct to alternatives, but the context implies it's for trending insights rather than personalized recommendations or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools have clearly distinct purposes: search, detail, comparison, similarity, dupes, recommendations, trends, and situational suggestions. The main potential confusion is between find_similar and find_dupes, since both return fragrances related to a given one, though their intent differs.
Tool names mostly follow a verb_noun snake_case pattern: compare_fragrances, find_similar, get_fragrance, search_fragrances. trending_fragrances and what_to_wear_tonight break the verb-first convention slightly, but the overall naming style is coherent and readable.
Eight tools is well-scoped for a fragrance discovery and recommendation service. Each tool covers a distinct user need without redundancy or bloat, and the count feels appropriate for the domain.
The tool set covers the full fragrance journey: searching, retrieving details, comparing, finding alternatives, personalized recommendations, trend awareness, and context-based picks. There are no obvious dead ends or critical missing operations for a read-focused recommendation API.