Find a shopper's period-underwear size in every brand from one hip measurement. Period underwear is sized on the hip, not a dress size, and brands disagree. Returns the size per brand plus any runs-small / runs-large note.
| Name | Required | Description | Default |
|---|---|---|---|
| hip_cm | No | Hip measurement in centimetres (alternative to inches). | |
| hip_inches | No | Hip measurement in inches (fullest part of hips and seat). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description fully discloses the input (hip measurement) and output (sizes per brand with notes). For a lookup tool, this is sufficient and transparent.
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?
Three sentences, each serving a purpose: goal, context, output. No extraneous information, front-loaded with key action.
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?
Complete for a simple lookup tool: input described, output format clear, reasoning for tool provided (brand disagreement). No output schema needed.
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?
With 100% schema coverage, baseline is 3. Description adds value by clarifying the two parameters are alternatives and only one needed, enhancing understanding beyond schema.
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 explicitly states the tool's goal: find period-underwear sizes across brands from a hip measurement. It clearly distinguishes from sibling tools which deal with absorbency or product search.
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?
Provides context on when to use (for period underwear sizing, noting it's hip-based not dress size). Lacks explicit when-not-to-use, but sibling differentiation is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool serves a distinct purpose: dataset provides raw data, translate converts between brands, find_products searches by capacity, and find_size returns sizing. No overlap.
Tools use mixed patterns: 'absorbency_dataset' and 'absorbency_translate' start with a noun, while 'find_products' and 'find_size' start with a verb. Inconsistent verb/noun order across the set.
Four tools cover the core domain of period underwear absorbency and sizing without being excessive or insufficient.
The set covers key operations: raw data access, cross-brand translation, product search by capacity, and size determination. Minor gaps like lack of brand-specific tier listing but overall sufficient.
