BraFinder

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

No annotations are provided, so the description bears the full burden. It describes the tool's behavior (returns fit notes or notable brands) but does not disclose data sources, accuracy, or any side effects. The description is adequate but not highly detailed.

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 a single sentence followed by usage examples—no wasted words. It is front-loaded with the core purpose and immediately followed by practical guidance.

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 simple tool with one optional parameter and no output schema, the description is complete: it explains when to use with/without the brand and what to expect. It does not detail return format, but that is acceptable for such a straightforward 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?

The input schema describes the single optional 'brand' parameter as 'A brand name (optional).' The description adds context: providing a brand returns its specific fit note, omitting it returns brands that run small or large. This adds value 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 tool's purpose: to determine how a bra brand runs relative to standard sizing in band and cup. It distinguishes itself from siblings like 'bra_size_dataset' (general sizing data) and 'translate_bra_size' (size conversion).

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 usage examples: 'Give a brand for its specific fit note, or omit to get the brands that most notably run small or large.' It covers both use cases but does not explicitly state when not to use or mention alternatives to sibling tools.

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 are provided, so the description carries the full burden. It describes the tool as returning 'key facts and the machine-readable feed URL,' which is transparent about the output but lacks details on format or potential side effects. Since it's a read-only data retrieval, the transparency is adequate but not rich.

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 consists of two concise sentences: one stating what the tool does and one specifying when to use it. Every sentence adds value without unnecessary detail.

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 zero parameters and no output schema, the description provides sufficient context: it identifies the resource and its use case. It lacks some detail on the exact structure of the returned dataset but is complete enough for an agent to understand the tool's role.

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 (schema covers 100% with no properties). The description implies no input is needed by stating 'Get... dataset,' which is sufficient. The schema already indicates no parameters, so the description adds value by confirming simplicity.

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 'Get BraFinder's cross-brand bra-size offset dataset', specifying a verb and resource. It also mentions key facts and a feed URL. This distinguishes it from siblings like translate_bra_size (individual conversion) and bra_brand_fit (fitting guide).

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 how bra sizes translate between brands,' providing a clear usage context. It does not explicitly state when not to use, but the context implies alternatives from sibling tools.

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; description covers real-time catalog and result format (price, links) but lacks details on empty results, caching, or other behaviors.

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 with no wasted words: first states purpose and key features, second gives example and output description.

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 given simple schema and no output schema; covers input format and output content, though default limit is not mentioned (handled by schema).

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 descriptions for both parameters; description adds usage example and context about result links, providing additional value.

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 states it finds in-stock bras by size with price, and differentiates from siblings (bra_brand_fit, bra_size_dataset, translate_bra_size) by focusing on inventory 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 clear input example ('Give a size like "34DD"') and mentions result links, but does not explicitly state when not to use or mention 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, the description carries full burden. It discloses that output includes equivalent size with fit note and confidence, and that omitting to_brand returns all brands. No side effects or destructive actions expected, making this 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?

Two sentences with key information front-loaded: verb, constraints, and use cases. Every sentence is necessary and efficiently written.

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 tool with 3 parameters and no output schema, the description covers all critical aspects: what it does, parameter semantics, output content, and usage scenarios. Sibling tools provide additional context.

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 description adds value by explaining optional behavior (to_brand: omit for all brands; from_brand: omit treats as US standard). Examples further clarify parameter usage.

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 ('Translate') and resource ('bra size'), and explicitly distinguishes from sibling tools by specifying cross-brand translation. Examples like 'what size am I in [brand]' solidify purpose.

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 use cases ('Use for...') and explains the non-equivalence of sizes across brands. Does not explicitly state when not to use, but the context is clear enough for an agent to infer.

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