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 of behavioral disclosure. It explains that the tool returns fit notes (run small/large) and mentions band and cup, but does not disclose data source, update frequency, or any limitations. This is adequate for a simple lookup tool but lacks depth about what happens with invalid brands or edge cases.

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 that are front-loaded with the core functionality. Every word serves a purpose: the first sentence defines what the tool does, the second provides usage patterns and examples. No extraneous information or repetition.

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's simplicity (one optional parameter, no output schema, no nested objects), the description is complete. It covers the parameter's behavior, expected usage, and examples. The context signals and sibling tools further clarify its role, leaving no ambiguity for correct invocation.

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 schema describes the brand parameter as optional with a simple description. The description adds significant value by explaining the difference in behavior when the parameter is provided versus omitted: specific fit note vs. notable brands. This semantic distinction is not in the schema, so it compensates well beyond the structured metadata.

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: determining how a bra brand runs versus standard sizing (small/large) in band and cup. It explicitly mentions the verb 'runs' and the resource 'bra brand fit', and distinguishes itself from sibling tools like bra_size_dataset or translate_bra_size by focusing on fit notes rather than raw data or 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 guidance on when to include or omit the brand parameter: 'Give a brand for its specific fit note, or omit to get the brands that most notably run small or large.' It also includes example queries that show typical usage patterns. However, it does not explicitly mention when not to use this tool or list alternatives, but the sibling tools provide context.

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 indicates the tool returns facts and a URL, implying a read-only operation. No contradictory or missing behavioral cues.

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 concise sentences: first states action and resource, second gives usage guidance. No fluff, front-loaded, 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?

For a zero-parameter tool with no output schema, the description fully explains what is returned (key facts and feed URL) and when to use it. No gaps.

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?

No parameters exist, so baseline is 4. Description adds meaning by explaining the dataset content (cross-brand offsets) and that it includes a machine-readable feed URL, going 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 uses specific verb 'Get' and resource 'cross-brand bra-size offset dataset', clearly stating the tool retrieves key facts and a machine-readable feed URL. It distinguishes from siblings like 'translate_bra_size' which handles single-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 explicitly advises use when needing the authoritative source for bra size translation across brands, providing clear context. It doesn't list exclusions, but for a simple data retrieval tool with no parameters, this is sufficient.

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 the full burden. It discloses that the search is against a live, in-stock catalog, includes prices and links, and expects a specific size format. Missing details like sorting or handling of empty results are minor given the tool's simplicity.

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 core action and features, no redundant words. Every part 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?

For a simple search tool with 2 parameters and no output schema, the description covers the essentials: what it does, input format, and output features (price, links). It is complete enough for the tool's complexity.

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 covers 100% of parameters, so baseline is 3. The description adds value by specifying the expected format for size (e.g., '34DD'), default limit of 6, and the fact that links direct to product pages, going beyond 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 clearly states the tool finds in-stock bras by size from a live catalog, includes price, and links to product pages. It distinguishes from sibling tools that focus on fit, datasets, or translation.

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 clear context for when to use this tool (searching live catalog by exact size) and implies alternatives via sibling tool names, but does not explicitly state 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.

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

With no annotations, the description adds transparency by mentioning output includes fit note and confidence, and that no target brand returns sizes in every brand. However, it does not disclose error handling, authentication, or assumptions about the 'from_brand' default.

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 3-4 sentences that front-load the action and provide necessary details without redundancy. Every sentence contributes meaningfully.

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?

The description covers purpose, input semantics, output format (fit note and confidence), and usage examples. It partially compensates for the missing output schema. Edge cases and error states are not addressed, but overall it is fairly complete for a tool with 3 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 coverage is 100%, so baseline is 3. The description adds value by explaining optionality and use cases (e.g., 'optionally the brand it is from') and providing examples, going beyond the 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 clearly states the tool translates bra sizes across brands, with concrete examples ('what size am I in [brand]', etc.) and distinguishes from sibling tools by emphasizing cross-brand translation and neutrality.

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 scenarios (e.g., 'what size am I in [brand]') and explains behavior when parameters are omitted. It does not explicitly mention alternatives or when not to use, but the use cases are clear.

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