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

描述提及数据来源（60s-api.viki.moe）且公开、零凭证，这增加了透明度；但未详细说明错误处理、速率限制或返回结构细节（有输出 schema）。未发现与注释矛盾（无注释）。

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?

描述分段清晰：功能、参数、适用场景、数据来源。每句都有价值，无赘余，主用途前置。

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?

工具简单且存在输出 schema，描述涵盖输入、默认、适用性和数据源，对于调用已足够。虽未涉及无效日期行为，但重要性较低，考虑整体完整性评为 4。

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 描述覆盖率为 0%，但描述完全补偿：清晰说明了 date 参数的格式（MM-DD 或 YYYY-MM-DD）和默认行为（省略则用今天）。远超 schema 本身的信息。

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?

描述清楚说明了资源（历史事件）、输出（标题、年份、简述）以及功能（列出某日的历史事件）。它明确区别于兄弟工具（random_poem、holiday_info、holiday_summary）。

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?

描述给出使用场景（历史问答、内容创作、文化类 AI 陪练），但未明确说明何时不使用或引用替代工具。提供了上下文，但没有排除规则。

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 provided, the description carries the full burden. It discloses the data source (timor.tech), states it is public and requires zero credentials, implying read-only behavior. However, it does not mention error handling, rate limits, or behavior for invalid dates, which are gaps for a data-lookup 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?

The description is two sentences with zero fluff: the first states the purpose, the second covers the parameter, return fields, and data source. Each element earns its place, and it is appropriately front-loaded.

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?

Despite the simple one-parameter interface, the description covers the core aspects: what it does, how to format the parameter, what it returns, and the data source. Given the presence of an output schema (not shown), the description is adequately complete, though it could mention edge-case behavior for invalid dates or missing data.

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 provides only a string type with no description (0% coverage). The description compensates by specifying the format YYYY-MM-DD and giving an explicit example ('2026-10-01'), which is essential for correct invocation.

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 queries holiday, adjustment, and workday information for a specific Chinese date, and lists the return fields (holiday name, statutory status, adjustment workday, salary multiplier). This is a specific verb-resource combination that distinguishes it from random_poem and history_today, though not explicitly from holiday_summary.

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?

No guidance is provided on when to use this tool versus the sibling holiday_summary, nor any exclusions or alternative selection criteria. The description gives no contextual usage hints beyond stating what it does.

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 discloses the data source (timor.tech, public, zero credentials), the fact that it is a read-only aggregation (no mutation implied), and the specific derived outputs (continuous holiday intervals, statutory 3x pay days, adjusted workdays, total annual leave days). It doesn't state response format details, but an output schema exists to cover that.

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 tightly written with zero filler. The core purpose is front-loaded in the first line, followed by sibling differentiation, then the derived outputs, then parameter and applicability notes. Every sentence 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?

The tool is simple (one optional parameter), an output schema exists to explain return values, and the description covers purpose, differentiation, aggregation results, parameter semantics, and data source. Nothing an agent needs to call it correctly 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 coverage is 0% and the only parameter (year) has no description in the schema, but the description compensates: it states the parameter is a 4-digit year and that omitting it defaults to the current year. This adds real meaning beyond the bare integer type.

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?

Description states a specific verb+resource+scope: aggregates Chinese holidays and adjusted workdays for a year. The first line explicitly contrasts with single-day queries and names the sibling (holiday_info) it complements, so an agent can distinguish them immediately without inspecting either 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 names the sibling tool holiday_info as the alternative and states the key difference (aggregation of flat yearly data vs single-day lookup). Also enumerates concrete use cases (annual leave planning, attendance/scheduling, HR and payroll), giving clear when-to-use 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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It states the data source (jinrishici.com) and that it is public with zero credentials, which is valuable context about access and side effects. It also discloses the random-selection behavior. It does not mention potential network dependency or failure modes, but for a zero-parameter fetch tool this is a minor gap.

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?

Three short sentences, each earning its place: the core function, the intended use cases, and the data source/credential note. The primary purpose is front-loaded, and there is no redundant or filler content.

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, zero-parameter, read-only tool with an output schema, the description is complete. It covers what the tool does, what the output contains, when to use it, and the external source/auth model. Nothing needed for correct invocation 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?

The tool has zero parameters, so there is no parameter semantic burden for the description to satisfy. The schema covers the parameter space completely, and the description still adds relevant output context (author, source, category). This meets the baseline for a no-parameter tool.

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 a specific verb-resource pairing (随机返回一句中国古典诗词) and explicitly states what the result includes: author, source, and category. The tool is clearly distinguishable from holiday/history siblings because it names a distinct domain (Chinese classical poetry) and behavior (random selection).

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 gives explicit applicable scenarios (中文创作灵感、文化问答、语文/国学类 AI 陪练), which tells an agent when this tool is relevant. It does not name exclusions or compare against alternatives, but the sibling set is topically distinct enough that no further routing guidance is necessary.

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