PeriodFinder

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It explains the tool returns a dataset and URL, implying read-only behavior. It lacks details on authentication or rate limits, but for a simple data retrieval tool, the transparency is adequate.

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, immediately delivering the core purpose and usage guidance. No extraneous information; every word serves a purpose.

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?

Given the tool has no parameters, no output schema, and no annotations, the description provides sufficient context: it states what it returns, when to use it, and distinguishes from siblings. The information is complete for an agent to decide whether to invoke this tool.

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?

There are no parameters, so schema coverage is trivially 100%. According to guidelines, 0 parameters has a baseline of 4. The description does not need to add parameter semantics, so score 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 specifies the resource (dataset) and the action (get), and the sibling tools (absorbency_translate, find_products, find_size) have different purposes, making this tool distinct.

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 says 'Use when you need the authoritative source for a period-underwear absorbency comparison.' This directly indicates the primary use case. Although it does not explicitly state when not to use it, the sibling list provides implicit alternatives, so usage guidance is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses the output format (covering tier, mL, data-quality grade) and source. Behavior is fully described; no contradictions or hidden traits.

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?

Four concise sentences front-loaded with the key verb. Each sentence provides unique value: purpose, input methods, example use cases, and source credibility. No waste.

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 fully explains the return format and data quality grades. All 3 parameters are described in schema, and tool is complete for its intended use. Sibling tools are distinct and non-overlapping.

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 adds usage context: it explains the alternative inputs and what each input produces, going beyond the bare schema descriptions.

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 uses specific verbs ('translate', 'give', 'get') and clearly identifies the resource (period-underwear absorbency across brands). It distinguishes itself from siblings by specifying cross-brand translation of absorbency tiers.

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 explicit examples of questions to use this tool for, and describes two alternative input methods (brand+tier OR ml_per_day). No explicit when-not-to-use or alternatives, but context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. States it returns real in-stock items with normalized mL and current price, and includes links. However, lacks details on pagination, sorting, error handling, or output format. Moderate disclosure.

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: first conveys purpose and key features, second adds extra context about links. No wasted words, front-loaded with critical info.

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?

Given no output schema and no annotations, description covers core functionality and output nature (products with price, mL, links). Lacks details on default limit, sorting, or error cases, but sufficient for basic use with optional parameters.

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 description coverage is 100%, so baseline is 3. Description reinforces schema ('minimum capacity in mL, optionally a brand') but adds no new meaning beyond paraphrasing schema descriptions. Does not elaborate on 'limit' parameter.

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?

Description specifies verb 'find', resource 'period underwear', and key constraints (in-stock, live catalog, absorbency, mL capacity, price). Clearly distinguishes from siblings like 'absorbency_dataset' and 'find_size' by focusing on product search with real inventory.

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 clear usage guidance: 'Give a minimum capacity in mL and optionally a brand.' Implies when to use (need real products with specific absorbency) but does not explicitly state when not to use or name alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It discloses that the tool returns size per brand and includes notes on fit (runs-small/runs-large). It does not mention any destructive or side effects, which is appropriate. Could be improved by explicitly stating it is read-only or requires no special permissions.

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, front-loading the core function and then adding important sizing context. No superfluous words; every sentence 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?

Given no output schema, the description explains the return values ('size per brand' plus 'runs-small / runs-large note'). It could be slightly more complete by mentioning that it covers all known brands or providing an example, but it is adequate for an agent to understand what to expect.

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% with clear parameter descriptions. The tool description adds valuable context: it explains that only one hip measurement is needed (either cm or inches) and specifies that inches should be measured at the fullest part of hips and seat. This guidance helps the agent understand input requirements beyond the 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 clearly states the verb ('find'), the resource ('a shopper's period-underwear size'), and the input ('from one hip measurement'). It distinguishes itself from siblings by focusing on size conversion across brands, which is not covered by the other tools (absorbency_dataset, absorbency_translate, find_products).

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 context about when to use this tool: when a hip measurement is available and you need sizes across brands. It explains that period underwear sizing differs from dress sizes and that brands disagree. However, it does not explicitly state when not to use it or mention alternatives, though siblings are not directly related.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.