Curated dupes for a fragrance — cheaper scents documented to smell like the original, with match percentage and price comparison. The answer to 'what smells like X without the price tag'.
| Name | Required | Description | Default |
|---|---|---|---|
| fragrance | Yes | Fragrance slug or name to find dupes for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal read-only, idempotent, non-destructive behavior, so the description adds meaningful context by explaining that results are curated, documented to smell like the original, and include match percentage and price comparison. This goes beyond the structured annotations and clarifies the curated nature of the data.
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?
Two concise sentences deliver the purpose, key output details, and the user-facing trigger phrase with no filler. The main behavior is front-loaded and every word contributes to understanding.
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 one-parameter tool with no output schema, the description adequately explains what the tool returns: curated dupes, match percentage, and price comparison. Combined with the clear input schema and supportive annotations, nothing essential is missing for correct invocation.
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 already covers the single parameter fully with a description ('Fragrance slug or name to find dupes for'), and schema coverage is 100%. The description adds no new parameter details beyond reinforcing that the input is a fragrance, so the baseline score of 3 is appropriate.
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's function: find curated, cheaper fragrance dupes for a given original, with match percentage and price comparison. It also distinguishes itself from siblings like find_similar by emphasizing 'dupes' and the 'without the price tag' angle, making the intended use unmistakable.
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 usage context: use this when someone asks for what smells like a fragrance at a lower price. It does not explicitly name alternatives or exclusion criteria, but the trigger phrase 'the answer to...' effectively routes the agent to this tool.
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.