Find real in-stock period underwear that covers a target absorbency, from the live catalog, with normalized mL capacity and current price. Give a minimum capacity in mL and optionally a brand. Links go to the PeriodFinder product page for each item.
| Name | Required | Description | Default |
|---|---|---|---|
| brand | No | Limit to one brand (optional). | |
| limit | No | How many products to return (default 6). | |
| ml_min | No | Minimum real capacity to cover, in mL (e.g. 40 for a heavy day). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description effectively discloses behavior: queries a live catalog, returns in-stock items with normalized mL capacity and price, and links to product pages. It doesn't mention any side effects or permissions, but the read-only nature is implied.
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 sentences, front-loaded with the core purpose, no unnecessary words. Every sentence adds value.
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?
With no output schema, the description partially explains the return value (links, normalized capacity, price) but does not specify the structure (e.g., array of objects) or all expected fields. Adequate but not fully complete.
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 coverage is 100% so baseline is 3. The description reinforces the ml_min and brand parameters but adds no new semantics beyond the schema. The limit parameter is not mentioned in the description.
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 finds real in-stock period underwear based on target absorbency and optionally brand. It distinguishes itself from sibling tools (absorbency_dataset, absorbency_translate, find_size) by focusing on product lookup from a live catalog.
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 implies usage for finding products with a minimum capacity and optional brand, but provides no explicit guidance on when to use this tool versus alternatives or when not to use it.
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.
