Kids Stay

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

No annotations are provided, so the description carries the burden of transparency. It discloses the content of the response (all conditions with per-facility evidence notes, kids' equipment, access, sources, verification date) and uses the verb '返します' indicating a read operation. It omits potential error behavior or side effects, but for a non-destructive detail fetch, the disclosure is quite good.

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, front-loaded with the key action ('slug指定で施設の全条件') and lists the returned categories efficiently. Every phrase adds value, with no redundancy or filler.

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 one-parameter tool with no output schema and no annotations, the description sufficiently covers what the tool returns and how the parameter is sourced. It lists all major output categories and mentions the slug origin, but it could be slightly more explicit about the intended workflow (e.g., 'use after search_hotels') to be 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?

The schema description covers the sole parameter 'slug' fully (stating it is included in search_hotels results), and schema coverage is 100%. The description repeats the fact that the parameter is a slug but adds no additional meaning beyond what the schema already provides, so baseline 3 applies.

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 a specific action: 'slug指定で施設の全条件...を返します' (returns all conditions... specified by slug). It distinguishes from sibling tools by showing it retrieves detailed verified information for a single facility, contrasting with search_hotels, list_areas, and list_conditions.

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 usage context is implied: the slug parameter comes from search_hotels results (as stated in schema), so this tool is clearly meant to be used after a search. However, the description does not explicitly state when to use this tool versus alternatives or mention any exclusions, leaving the guidance implicit rather than direct.

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 clearly states it 'returns' data, implying a read-only operation, and lists the content types. However, it does not disclose potential behavior like response size, filtering, or data freshness, leaving some ambiguity.

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, with the return value stated first and usage context second. Every word earns its place with no fluff.

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 list tool with no output schema, the description covers the main return items and usage context. It could be more explicit about whether it returns all prefectures or only major ones, but overall it is sufficient.

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 zero parameters, so the baseline is 4. The description adds no parameter information but doesn't need to since there are none, and schema coverage is 100% trivially.

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 it returns facility counts by prefecture, major areas, and editorial area feature pages (返します). The verb and resource are specific, and it is distinguishable from sibling tools like search_hotels which focus on hotel 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?

The description provides usage context with '検索前の当たり付けに使います' (use for preliminary targeting before searching), indicating when to use it. However, it does not explicitly name alternatives or exclusions, so it stops short of a 5.

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 of disclosing behavior. It describes a read operation ('returns') but does not explicitly mention safety, ordering, or exhaustiveness. For a simple list tool, this is adequate but minimal.

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 that front-loads the key information (returns all definitions) and lists the fields. Every word is purposeful with no filler.

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 covers the essential details: what is returned and the fields. It could more explicitly connect to search_hotels, but the 'search usable' note provides sufficient 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?

The tool has zero parameters, so there are no parameter semantics to clarify. The description adds value by enumerating the output fields, which is helpful despite the lack of parameters.

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 returns all definitions of child-friendly search conditions, specifying the fields (id, label, target age, number of applicable facilities). The verb '返します' (returns) is specific, and the resource is distinct from sibling tools like get_hotel and list_areas.

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 phrase '検索に使える' (usable for search) implies the conditions are meant for use with search_hotels, but it doesn't explicitly state when to use this tool versus alternatives like list_areas. Usage context is implied rather than explicit.

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 must carry the behavioral disclosure burden. It reveals that results include the page URL for details/reservation and that conditions are verified, but it does not mention whether the operation is read-only, any rate limits, or what happens with missing filters. The description adds some useful context but omits safety and side-effect information.

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 concise sentences that front-load the core purpose, then provide the keyword alternative and result URL. Every phrase contributes meaning, with no redundancy or fluff.

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 8 parameters, no output schema, and no annotations, the description provides a solid overview of the search capability, references the list_conditions sibling, and highlights the key output (page URL). It does not explain default sort or no-result behavior, but such details are covered by the schema for sort/limit. Overall, it is complete enough for an AI agent to understand when and how to use the 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?

Schema coverage is 100% with detailed descriptions for all 8 parameters, so the description does not need to repeat them. It adds value by clarifying that conditions are verified, that list_conditions provides the valid set, and that keyword supports partial matching and hotel-name lookups. However, this is marginal enrichment beyond an already comprehensive 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 searches Japanese hotels using verified child-friendly conditions (48 types), area, child age, and price. It explicitly references the sibling tool list_conditions and distinguishes direct keyword search when the hotel name is known. This differentiates it from get_hotel and other siblings.

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 usage context: it tells users to refer to list_conditions for the 48 condition types and to use keyword for direct hotel-name searches. It does not explicitly state when to use alternatives like get_hotel, but the reference to list_conditions and the keyword path give practical guidance.

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