Japanese holiday and business-day calculations using official Japan Cabinet Office data.
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Average 4.4/5 across 4 of 4 tools scored.
Each tool has a distinct purpose: adding business days, counting between dates, checking holiday status, and listing holidays. No overlap or ambiguity.
All tool names use snake_case with a consistent verb_noun or is_ pattern (add_business_days, business_days_between, is_holiday, list_holidays).
4 tools is appropriate for a calendar/holiday server, covering the essential operations without being too sparse or overloaded.
The tool set covers core business day arithmetic (add, count) and holiday information (check, list). Negative days and calendar options are included, so no obvious gaps.
add_business_daysAInspect指定した日付(date)の翌日から数えてN営業日目(days)の日付を返します。dateの当日は数えません。daysが負の場合は過去方向（dateの前日から遡ってN営業日目）に計算します。土日・日本の祝日を除外し、calendar="banking"を指定すると銀行休業日（12/31〜1/3）も除外します。dateが営業日かどうかだけ知りたい場合は is_holiday を使ってください（days=0でも同じ情報がskipped経由で得られます）。入力例: {"date": "2026-07-10", "days": 3}
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | 起点日 YYYY-MM-DD。例: "2026-07-10"。この日自体は数えません。 | |
| days | Yes | 何営業日先(正)/前(負)かを表す整数。0も指定可: dateをそのまま返し、dateが非営業日ならskippedに1件（date自身とその理由）を記録します。 | |
| calendar | No | "standard"（既定）または"banking"（銀行休業日も除外）。 | |
| extra_closed_dates | No | 顧客固有の追加休業日（YYYY-MM-DD の配列）。例: ["2026-12-29", "2026-12-30"] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and delivers: counting starts the day after the given date, negative days go backward, weekends and Japanese holidays are excluded, the 'banking' calendar adds bank holidays, and days=0 behavior is explained with the skipped field. This is comprehensive behavioral 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?
The description is compact yet information-dense, front-loading the core operation, then covering edge cases, calendar options, an alternative tool, and an example—all without unnecessary words.
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 that there is no output schema, the description explains the result and mentions the skipped field, but the exact return structure (e.g., whether it's a plain date string or an object with metadata) is not fully specified. It covers negative days, zero days, calendar variants, and alternatives, so it is quite complete overall.
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 the baseline is 3. The description adds meaning beyond the schema: the starting date is not counted, negative days indicate backward calculation, calendar options are explained, and days=0 semantics are clarified. It doesn't add new meaning for extra_closed_dates, but the schema already documents it.
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 function: returning the date that is N business days after or before a specified date, with explicit counting rules. It distinguishes from the sibling is_holiday by directing users to that tool for simple business-day checks.
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?
It explicitly says to use is_holiday if only checking whether a date is a business day, and notes that days=0 can also provide that via skipped. However, it does not mention business_days_between or list_holidays, so sibling differentiation is not complete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
business_days_betweenAInspectfrom〜to間の営業日数を数えます。既定では「fromを含まず、toを含む」境界です（例:「今日から支払期日まで何営業日か」）。include_from/include_toで境界を変更できます。土日・日本の祝日を除外し、calendar="banking"を指定すると銀行休業日（12/31〜1/3）も除外します。from > to の場合はエラーにせず、from/toとinclude_from/include_toを入れ替えて計算した結果の符号を反転した値（負の営業日数）を返します（反対称則: count(A,B,x,y) = -count(B,A,y,x)。A≠B、すなわちfrom≠toの場合に成立する恒等式で、from===toはこの分岐に入らないため対象外）。入力例: {"from": "2026-07-10", "to": "2026-07-20"}
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | 終点日 YYYY-MM-DD。例: "2026-07-20"。fromより前の日付も指定可（結果は負の営業日数になります）。 | |
| from | Yes | 起点日 YYYY-MM-DD。例: "2026-07-10"。 | |
| calendar | No | "standard"（既定）または"banking"（銀行休業日も除外）。 | |
| include_to | No | toを営業日数に含めるか。既定はtrue。 | |
| include_from | No | fromを営業日数に含めるか。既定はfalse。 | |
| extra_closed_dates | No | 顧客固有の追加休業日（YYYY-MM-DD の配列）。例: ["2026-12-29", "2026-12-30"] |
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 thoroughly discloses default boundary semantics (from excluded, to included), weekend/holiday exclusion, optional banking calendar, and the antisymmetry behavior when from > to, including a formula and its applicability condition. This is rich, non-obvious behavioral context.
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 somewhat long but each sentence contributes essential information: purpose, boundary defaults, calendar options, and edge-case behavior. It is well-structured with a clear lead, though the antisymmetry formula could be seen as slightly over-detailed for a general audience.
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 6 parameters, no output schema, and no annotations, the description covers all key aspects: purpose, default behavior, configuration, edge cases, and an example. It does not explicitly state return type or invalid-date behavior, but these are minor gaps given the tool's scope.
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 has 100% coverage, describing each parameter individually. The description adds global context (default boundary, reversal behavior) but does not significantly enhance meaning for individual parameters beyond what the schema already provides. The baseline 3 is appropriate given high schema coverage.
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 counts business days between two dates, using a specific verb ('数えます') and resource ('from〜to間の営業日数'). It distinguishes from siblings by focusing on counting rather than adding, checking, or listing holidays, and includes boundary and reversal details.
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 a concrete use case ('今日から支払期日まで何営業日か') and explains configuration options (include_from/include_to, calendar). It does not explicitly name alternatives or state when not to use it, but the purpose is clear enough that an agent can infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
is_holidayAInspect指定した日付が日本の祝日（国民の祝日・休日）かどうかを判定します。内閣府公表データに基づきます。入力例: {"date": "2026-01-01"}
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | YYYY-MM-DD形式の日付。例: "2026-01-01" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry full behavioral burden. It mentions data source (Cabinet Office) but omits details like return format, error handling, or what constitutes a holiday. It does not contradict annotations (none present).
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 long, front-loads the purpose, and includes a concrete example without wasted words.
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 (single parameter, no output schema), the description is complete. It states what it does, the data source, and gives an example input.
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% for the single parameter 'date', which already specifies format and example. The description adds an input example and context (Japanese), providing marginal 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 if a date is a Japanese public holiday. It specifies the verb (判定), the resource (date), and the scope (Japanese holidays based on Cabinet Office data). The example input further clarifies usage.
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 does not explicitly state when to use this tool versus its siblings (add_business_days, business_days_between, list_holidays). Usage can be inferred (single-date check vs. listing), but no direct guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_holidaysAInspect指定した年または期間の日本の祝日一覧を返します。入力例: {"year": 2026} または {"from": "2026-01-01", "to": "2026-06-30"}
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | 期間終了日 YYYY-MM-DD。例: "2026-06-30"。yearとは併用不可。 | |
| from | No | 期間開始日 YYYY-MM-DD。例: "2026-01-01"。yearとは併用不可。 | |
| year | No | 西暦年。例: 2026。from/toとは併用不可。 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes a straightforward read operation with no side effects or hidden behaviors. It does not disclose authorization needs or response format, but for a simple list retrieval, it is sufficiently transparent.
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 examples, extremely concise and front-loaded. Every element is necessary and contributes to understanding.
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 simplicity of the tool (no output schema, few parameters), the description is adequately complete. It explains the input options and what is returned. It could mention the return format, but it is not essential 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?
Schema coverage is 100% with detailed parameter descriptions including mutual exclusivity. The description adds value beyond the schema by providing concrete input examples, reinforcing the parameter usage and format.
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 that the tool returns a list of Japanese holidays for a specified year or period. It uses a specific verb ('返します') and resource ('日本の祝日一覧'), and the examples distinguish it from sibling tools which handle business days and single-date checks.
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 with input examples and implies when to use (to get holiday lists). It does not explicitly exclude scenarios or mention alternatives, but the sibling tools have distinct purposes, so the agent can infer appropriate usage.
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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