2027 連假與訂票

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

No annotations are provided, so the description carries the burden. It usefully discloses what is evaluated (crowd level derived from national holidays and peak season), but says nothing about how the level is expressed, auth needs, or rate limits. Some real value, but incomplete for a zero-annotation tool.

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?

A single front-loaded sentence names the purpose first and appends the country-code list. No filler, though the enum list makes it denser than ideal for skimming.

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 3-param read tool with no annotations or output schema, the description covers what is computed, the date handling, and the country domain. The only gap is not spelling out the returned crowd-level scale, which 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?

The schema documents start/end as YYYY-MM-DD but leaves country undescribed. The description compensates by enumerating the accepted country codes (JP, KR, HK, CN, VN, TH, SG, PH, ID, US, GB, FR, AU), which the schema lacks entirely — a genuine addition beyond structured data.

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 (查/check) and resource (人潮等級/crowd level) for a destination over a date range, and clarifies it evaluates national holidays and peak season. It does not explicitly differentiate from siblings like flight_booking_timing or get_2027_long_weekends, but the purpose is discernible.

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?

Usage is only implied — the tool's function (checking whether dates hit local holidays/peak season) suggests a pre-booking check, but there is no explicit when-to-use, when-not-to-use, or named alternative among the siblings. Adequate but leaves routing to inference.

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 disclosure burden. It usefully reveals that the output is an advisory recommendation (建議購票期) rather than live pricing data, which tells the agent not to treat results as fare quotes. It says nothing about data freshness, coverage limits, or that peak-season/holiday logic is applied implicitly, so the behavioral picture remains partial.

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?

One compact sentence front-loads the decision to be answered, then the inputs used, then the exclusion clause. Every clause earns its place with no redundant restatement of the tool name.

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 two-parameter, annotation-free, output-schema-free tool, the description covers what it returns (a recommended booking window) and what it is not (real-time fares), and it explains that holidays and peak season are folded into the logic even though they are not parameters. Only the absence of any sibling routing keeps it from being 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?

Schema description coverage is 100% (depart = YYYY-MM-DD, dest = 國碼), so the parameters are already documented and the baseline is 3. The description adds that depart is the 出發日 driving the recommendation and that destination factors in, but it does not clarify format expectations or optionality of dest 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?

States a specific decision verb and resource: evaluate whether to buy or wait for a Taiwan-departing flight and return a recommended booking window. It also scopes out a capability (不是即時票價 / not real-time fares), which prevents misuse, but it never names a sibling such as ticket_booking_guide to differentiate itself within the toolset.

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 decision framing (該買還是該等) implies the usage context of timing a purchase before searching fares, and 不是即時票價 excludes a nearby use case. However, there is no explicit statement of when to choose this over ticket_booking_guide, check_destination_crowd, or plan_leave_2027, so routing is left to inference.

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, but it does disclose meaningful traits: this is a read-only reference dataset, fixed to Taiwan 2027, enumerating 9 holidays and their best leave combinations. It does not say whether the data is static/curated, its update cadence, or the exact return structure, so a mutation-free read is only inferred from the name's 'get_' prefix.

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?

A single front-loaded sentence that states scope first (which year, which country, how many holidays) and then the secondary value-add (optimal leave combinations) with a concrete example. No filler or redundant restatement of the tool name.

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?

With no parameters, no annotations, and no output schema, the description must convey what comes back, and it does: 3+ day holiday blocks plus per-holiday leave-bridging suggestions for 2027. Coverage is adequate for a zero-argument reference tool, though exact field formats and the distinction from sibling plan_leave_2027 remain unstated.

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 takes zero parameters, which per the rubric sets a baseline of 4; there is nothing for the description to disambiguate. It correctly adds no parameter chatter, and the examples given (leave 1-5 days, Spring Festival example) relate to output content rather than inputs.

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 name plus description state a specific resource and scope: the 9 long weekends of 3+ days in Taiwan 2027 (ROC 116), plus bridging-leave combinations for each. It goes beyond the bare name by quantifying the dataset (9 holidays, 1-5 leave days) and giving a concrete example (Spring Festival: take 5, get 16 off). It does not, however, distinguish itself from the near-identical sibling plan_leave_2027.

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?

Usage is only implied: an agent can infer this is the lookup for 2027 holiday dates and leave-stacking options. There is no explicit when-to-use statement and no mention of how this differs from plan_leave_2027, which appears to cover overlapping territory.

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 burden. It does disclose the essential behavior and output (longest consecutive break plus the days to request), which is useful. It does not state whether results are deterministic, what the response looks like structurally, or any constraints beyond the input range already in the schema.

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?

A single front-loaded sentence that states the input, the computation, and the output with zero filler. Nothing can be trimmed without losing meaning.

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?

There is no output schema, so the description must convey the return; it does so at a high level (longest combination and which days to request). For a simple one-parameter computation tool this is nearly complete, with only the exact output shape left unspecified.

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% and the sole parameter 'leave' is already documented (可請天數 1～10). The description restates the input meaning (可請的假日天數) without adding format or edge-case detail beyond the schema, so the baseline of 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 states a specific verb and resource: it computes (算出) the longest consecutive holiday combination in 2027 and which days to request off, given a number of leave days. It is clearly distinguishable from siblings like get_2027_long_weekends, though it does not name or contrast with 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?

Usage is implied by the input sentence (enter how many leave days you have available), so an agent can infer when to call it. However, there is no explicit when-to-use vs when-not guidance, and no mention of related tools such as get_2027_long_weekends that might be used together with 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not state that this is a read-only reference lookup, whether the content is static or updated, or what form the guidance is returned in; it only names subject areas.

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?

A single front-loaded sentence that packs the covered topics tightly with no filler. It is efficient, though the colon-delimited topic dump slightly blurs rather than clarifies the tool's function.

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, no-output-schema knowledge tool, listing the covered topics is enough to make a rough call, and the Taiwan scope is stated. It still omits the scope boundaries (e.g., only immersive theater and escape rooms, not general ticketing) and the nature of the returned content, leaving the agent to infer coverage limits.

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 takes zero parameters, so the baseline is 4. The description does correctly signal that the guide is scope-free (no input needed to select a venue, date, or platform), which is consistent with the empty 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 enumerates the content the guide covers (immersive theater/escape room booking, Taiwan ticketing platforms, refund rules, scalper penalties, pre-booking checklist), so an agent can infer it is a static informational resource. However, it never states the verb or form of the deliverable — it reads as a topic list rather than a clear statement of what the tool returns. It is distinguishable from the travel/timing siblings by domain, but only implicitly.

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?

There is no indication of when an agent should call this versus the sibling tools (check_destination_crowd, flight_booking_timing, plan_leave_2027), nor any prerequisites or exclusions. The reader must guess the triggering condition from the topic list alone.

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