Fragrances most similar to a given one, from Perfume Picks' precomputed similarity ranking over notes and accords.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 5) | |
| fragrance | Yes | Fragrance slug or name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal readOnly, idempotent, and non-destructive behavior. The description adds useful context beyond that by stating the similarity is precomputed and based on notes and accords, indicating a deterministic, offline ranking rather than live user-specific filtering.
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 one tight sentence with no wasted words. It front-loads the main purpose and then gives the distinguishing source of similarity, making it easy for an agent to parse quickly.
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 two-parameter read-only lookup, the description provides enough context: what it returns, how similarity is determined, and the input basis. It does not describe the exact output structure, but the notion of a similarity ranking makes the return shape reasonably inferable.
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 description coverage is 100%, so both parameters (fragrance and limit) are already documented in the schema. The description adds no extra parameter-level meaning, which is acceptable given the schema carries the load.
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 returns fragrances similar to a given fragrance, based on a precomputed similarity ranking over notes and accords. It does not use an explicit verb like 'returns' or 'lists,' and it doesn't directly contrast with siblings like find_dupes or get_recommendations, though the precomputed ranking over notes/accords helps distinguish it.
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 context is implied: use this tool when you need similar fragrances to a specified one. However, there is no explicit guidance about when not to use it or which sibling tool would be a better choice for related but different needs.
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.