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PeriodFinder

by io.github.fenrici1

absorbency_translate

Translate 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierNoThe brand's absorbency tier word, e.g. Light, Moderate, Heavy, Super, Overnight.
brandNoA period-underwear brand, e.g. Thinx, Knix, Saalt, Modibodi, WUKA.
ml_per_dayNoTarget real capacity in mL/day (alternative to brand+tier). A regular tampon holds about 5 mL, a super about 9 mL.

TDQS

A4.3/5.0
Behavior4/5

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.

Conciseness4/5

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.

Completeness4/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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TDQS

A4.1/5.0
Disambiguation5/5

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.

Naming Consistency2/5

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.

Tool Count5/5

Four tools cover the core domain of period underwear absorbency and sizing without being excessive or insufficient.

Completeness4/5

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.

Resources