PlugRail Japanese Calendar

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

With no annotations, the description carries the full burden and does so well: it discloses exclusion rules (weekends, Japanese holidays, and banking holidays 12/31-1/3 with calendar="banking"), the non-inclusive counting of the anchor day, and negative-direction behavior.

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?

Front-loaded with the core semantics and finishing with a concrete input example. Some counting details are stated twice (anchor day not counted appears in both description and schema), but overall dense and low-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?

No output schema exists, and the description only alludes to the 'skipped' return mechanism without fully describing the response shape. For a deterministic computation tool the behavioral and parameter coverage is otherwise complete, so the gap is minor.

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 date, days, calendar, and extra_closed_dates are already fully documented in the schema. The description reinforces days/calendar behavior but adds little not already in the schema, warranting the baseline 3 for a high-coverage 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?

States a specific verb+resource: returns the date N business days from (but not counting) the given date, with explicit forward/backward semantics. It names and distinguishes itself from the sibling is_holiday, so an agent can route without opening a schema.

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?

Explicitly routes the agent: 'if you only want to know whether date is a business day, use is_holiday instead'. It also notes days=0 yields the same info via skipped, giving clear when-to-use and when-to-use-something-else guidance.

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 and does so well: it discloses the default excluded-boundary rule, weekend/Japanese-holiday exclusion, the added banking closure range (12/31〜1/3), and — critically — that from > to returns a negative count instead of erroring, including the antisymmetry identity and its from≠to precondition. That is exactly the non-obvious behavior an agent could not infer.

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?

Core purpose and default boundary are front-loaded, and the input example at the end is immediately usable. The antisymmetry parenthetical is verbose and largely answers a question few agents will ask, costing it the top score.

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 6-parameter, annotation-free tool the description covers boundary semantics, exclusion rules, and edge-case return behavior; the remaining parameters are fully documented in a 100%-covered schema, and no output schema means the numeric return is implicit in "営業日数を数えます". It stops just short of stating the return type explicitly or mentioning extra_closed_dates.

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, but the description adds meaning beyond the schema: it explains why include_from defaults false and include_to true via a worked example, and specifies the concrete dates added by calendar="banking" where the schema only says "銀行休業日も除外". Only extra_closed_dates is left entirely to 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?

States a specific verb and resource (count business days between from and to) and immediately scopes it with the default boundary semantics. It is clearly distinguishable from siblings add_business_days (shifting a date), is_holiday (single-date test), and list_holidays (enumeration), without needing to open any schema.

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 a concrete use scenario ("今日から支払期日まで何営業日か") and tells the agent how to alter behavior via include_from/include_to and calendar="banking". However, it never explicitly routes the agent away from or toward named alternatives, so it stays at clear-context rather than explicit when/when-not.

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 does add useful provenance ('based on Cabinet Office published data'), implying an official, read-only lookup, but it does not disclose the supported date range of that dataset, behavior for malformed/out-of-range dates, or whether the result is a boolean.

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?

Front-loads the purpose in one sentence, follows with the data source, and closes with a compact example. No wasted prose, though the example duplicates the schema's own example.

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 one-parameter predicate tool with no output schema and no annotations, the description covers purpose, data source, and input shape, but does not state the return value (boolean) or the temporal coverage of the holiday data — the two things an agent would most want to know before calling.

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% and the sole 'date' parameter is fully documented with format and example. The description only repeats an input example already present in the schema, adding no new semantics, so the 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?

States a specific verb+resource: judging whether a given date is a Japanese national holiday, grounded in Cabinet Office data. It is distinguishable from list_holidays (which enumerates) and the business-day arithmetic siblings, though it does not name them explicitly.

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 single-date yes/no framing implies when to use it versus list_holidays, but there is no explicit when-to-use or when-not-to-use statement, and no mention of date-range coverage limits of the underlying data.

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 behavioral burden. It establishes that this is a read operation returning a list and scopes the data to Japanese holidays only, which is useful context, but says nothing about error handling for contradictory inputs, out-of-range years, or result ordering.

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: the purpose statement is front-loaded, followed immediately by concrete call examples. No filler, though the examples mildly duplicate the schema's own parameter documentation.

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 three-parameter read-only lookup with no output schema and full schema coverage, the description is largely sufficient. The only minor gap is that the year/from-to mutual exclusivity is left to the schema rather than restated, but nothing an agent needs to select or invoke the tool is missing.

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 the schema already documents each parameter's format and the year vs. from/to mutual exclusivity. The description's inline examples illustrate valid combinations but add no format or syntax detail beyond what the schema provides. Baseline 3 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?

States a specific verb and resource ("日本の祝日一覧を返します" – returns a list of Japanese holidays) with clear scope (year or date range). It is easy to distinguish from siblings like is_holiday or add_business_days, though it never names those alternatives. Clear but not fully sibling-differentiating.

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 two input examples imply the two supported modes (single year vs. date range), giving implied usage guidance. However, there is no explicit statement of when to prefer this over is_holiday or business_days_between, and no exclusion conditions beyond what the schema already encodes.

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