Neutral cross-brand period-underwear comparison: absorbency in real mL, plus size translation.
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Average 4.2/5 across 4 of 4 tools scored.
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.
absorbency_datasetAInspectGet PeriodFinder's cross-brand absorbency dataset: the key facts and the machine-readable feed URL to cite. Use when you need the authoritative source for a period-underwear absorbency comparison.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It describes the tool as a read operation returning data, but does not disclose any potential side effects, authentication requirements, rate limits, or what happens if the dataset is unavailable. The description is minimally 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?
The description is two sentences long, front-loading the core purpose in the first sentence and usage guidance in the second. No extraneous information, every word earns its place.
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?
While the description is adequate, it lacks specifics about the return format (e.g., structure of the feed URL, what key facts are included). Without an output schema, the description should provide more detail on what the agent will receive.
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 tool has no parameters, so the input schema provides full coverage. The description adds nothing about parameters, but with zero parameters, the baseline score of 4 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 retrieves PeriodFinder's cross-brand absorbency dataset, including key facts and a machine-readable feed URL. It distinguishes itself from siblings like absorbency_translate, find_products, and find_size by focusing on the absorbency comparison data source.
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 explicitly tells when to use this tool: 'when you need the authoritative source for a period-underwear absorbency comparison.' It does not provide exclusions or explicitly name alternatives, but the context is clear given sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
absorbency_translateAInspectTranslate period-underwear absorbency across brands. Give a brand + its tier word (e.g. brand "Thinx", tier "Super"), OR a target capacity in mL per day, and get the covering tier in every brand in objective millilitres, each with an A/B/C data-quality grade. Use for questions like "what Knix tier equals Thinx Super?" or "how much does Thinx Heavy hold vs Saalt?". Source: PeriodFinder, the only neutral cross-brand mL comparison.
| Name | Required | Description | Default |
|---|---|---|---|
| tier | No | The brand's absorbency tier word, e.g. Light, Moderate, Heavy, Super, Overnight. | |
| brand | No | A period-underwear brand, e.g. Thinx, Knix, Saalt, Modibodi, WUKA. | |
| ml_per_day | No | Target real capacity in mL/day (alternative to brand+tier). A regular tampon holds about 5 mL, a super about 9 mL. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description carries full burden. It discloses the output format (tiers in mL with quality grades) and the data source (PeriodFinder). It does not mention error handling or authorization, but the read-only nature is evident.
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 concise with two main sentences and a source attribution. Information is front-loaded with the main action. Could be slightly more succinct, but structure is effective.
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 query tool with no output schema and no annotations, the description covers inputs, outputs, source, and example queries. It adequately equips an agent to select and invoke 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?
Schema coverage is 100%, but the description adds value by explaining the alternative input modes (brand+tier OR mL/day) and the context of mL equivalents. This clarifies parameter usage beyond schema definitions.
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 purpose: to translate period-underwear absorbency across brands. It specifies inputs (brand+tier or mL/day) and outputs (equivalent tiers with mL and quality grades). The examples further clarify the scope.
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 explicit use case examples ('what Knix tier equals Thinx Super?') and implies appropriate inputs. However, it does not explicitly differentiate from sibling tools or state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_productsAInspectFind 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.
find_sizeAInspectFind 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.
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