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

Annotations already mark the tool as read-only and open-world, and the description adds useful behavioral context: it forms clusters based on title, keyword, and time proximity, and it promises not to generate facts that cannot be traced. This extra grounding/hallucination-avoidance guarantee is valuable beyond the annotations. It does not mention pagination or response shape, but the annotation coverage lowers the burden.

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 compact Chinese sentence with no redundant filler. It front-loads the primary behavior before adding the traceability guarantee, and every part contributes either to purpose or to an important behavioral constraint.

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 has seven optional parameters, no output schema, and no parameter-level documentation, yet the description gives only a high-level behavior summary. It does not state what the returned event clusters look like, how limits and sorting affect output, what date range defaults apply, or how source filtering behaves. For an agent to invoke this tool correctly, significantly more contextual detail is needed.

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 0%, and the description provides no meaningful parameter-level guidance. It only implies that some query text is involved and that time/cluster criteria matter, but it does not explain how 'from', 'to', 'sort', 'limit', 'category', 'source_ids', or 'query' should be used. With seven parameters and no schema descriptions, the description fails to compensate for the missing parameter semantics.

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: query news and group it into event clusters using similar titles, keywords, and temporal proximity. This distinguishes it from sibling search tools that retrieve individual articles rather than aggregating them into clusters. Even without naming a sibling, the clustering mechanism makes the tool's purpose concrete.

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 context is implied rather than explicit. The clustering language suggests this tool is appropriate when an agent needs grouped event coverage rather than a flat search result list, and 'China' in the name suggests geographic scope. However, it never explicitly says when to prefer aggregate_china_news over search_china_news or aggregate_international_news, and no exclusions or alternative routing are provided.

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?

Annotations already provide readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds meaningful behavioral context by specifying the clustering criteria (similar titles, keywords, temporal proximity) and that the output consists of traceable event clusters, which is valuable in the absence of an output schema. It does not fully explain cluster structure or traceability mechanics, but it goes beyond the annotations.

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 one efficient sentence, front-loaded with the primary action and then the clustering behavior. There is no filler, repetition, or redundant detail, making it appropriately concise.

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 8 parameters, no output schema, and no parameter descriptions, a single-sentence description is insufficient for reliable invocation. It omits how time ranges are specified, what the returned clusters contain, how 'traceable' is expressed, and how filters such as category, language, and source_ids interact with clustering.

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 0%, so the description should compensate, but it does not explain any of the 8 parameters. Words like '查询' and '关键词' only weakly hint at a keyword query, while from, to, sort, limit, category, language, and source_ids receive no elaboration. An agent must rely entirely on parameter names and schema defaults.

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 (查询, query) and resource (国际新闻, international news), then explains that it forms traceable event clusters based on similar titles, keywords, and temporal proximity. This clearly differentiates it from sibling tools like search_international_news, which retrieves individual articles, and aggregate_china_news, which is China-specific.

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 clustering behavior implies the tool is intended for grouped-event discovery rather than simple search, so usage context is partially inferable. However, the description does not explicitly say when to prefer this tool over alternatives like search_international_news, nor does it mention any exclusions or when-not-to-use conditions.

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?

Beyond the readOnlyHint and openWorldHint annotations, the description adds meaningful behavioral constraints: it respects robots.txt, does not bypass logins or paywalls, and only works on registered sources or recent search result pages. This clarifies important access limitations and ethical boundaries that the annotations alone do not convey.

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 front-loaded sentence that states the verb, resource, scope, and behavioral constraints with no redundant wording. Every clause contributes useful information.

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 description covers the core extraction scope and ethical constraints, which is good for a simple read-only tool. However, with no output schema and no mention of max_chars behavior or response shape, an agent still faces some ambiguity about the returned content and how to control its length.

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 0%, so the description must compensate, but it does not mention max_chars at all and gives no guidance on how the optional parameter controls output length. The url parameter is inferable from the schema and tool name, but the only non-obvious parameter is left undocumented.

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 ('extract') and resource ('news article body text') and narrows the scope to registered sources or recent search result pages. This clearly distinguishes it from the sibling search/aggregation tools, which retrieve or list articles rather than extracting content from a URL.

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 clear context: this tool is for extracting content from already-known sources or pages found in recent search results, implying it is used after discovery via the search siblings. It does not explicitly name alternative tools or state when not to use it, but the context is sufficient for an agent to select it appropriately.

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?

Annotations declare readOnlyHint and openWorldHint, and the description adds relevant behavioral context: it preserves per-platform rank and heat, detects cross-platform similar topics, and warns that heat values are only comparable within the same platform. This goes beyond the annotations without contradicting them.

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 and front-loaded, with two sentences that each add useful information: what platforms are covered, what output is retained, and the critical cross-platform heat caveat. No filler or redundant schema repetition.

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 description covers the core output semantics (rank, heat, similarity grouping) and an important interpretation caveat. However, with no output schema and three undocumented parameters, the tool definition leaves the agent to guess how limit and query affect results and what the response shape is beyond the general mentions.

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 0%, so the description must compensate for explaining limit, query, and source_ids. It only partially hints at platform selection by naming weibo, toutiao, and baidu, but omits 'x', and gives no guidance on what limit or query control. This is a significant gap.

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 names a specific action ('查询') with a clear resource ('多平台热榜') and lists concrete platforms (微博热搜、今日头条热榜、百度热搜). It also distinguishes itself from the news search/aggregation siblings by focusing on trending rankings and cross-platform similarity.

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 intended use case is implied: retrieve trending topics from multiple Chinese platforms. However, there is no explicit guidance about when to prefer this tool over siblings like search_china_news or aggregate_china_news, nor any exclusion criteria.

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?

Annotations already cover the read-only safety profile (readOnlyHint=true), so the bar is lower. The description adds a useful behavioral detail by disclosing that the tool reports recent run status alongside the source lists. However, it leaves ambiguous what '最近一次运行状态' refers to (last run of which process?) and gives no detail on output ordering or volume. No contradiction with annotations.

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 sentence that front-loads the verb and resource and compactly enumerates all four returned categories. Every element carries information; there is no filler or redundancy.

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 output schema, the description bears the burden of explaining return values, and it does so by enumerating the four output categories (sources, hot list sources, supported categories, last run status). This is sufficient for a zero-parameter listing tool. Minor gap: it never explicitly frames the tool as the discovery step for the sibling search/aggregate tools, and the run-status item is underspecified.

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 schema is fully covered and no parameter semantics need explanation. Baseline 4 is appropriate; there is nothing the description fails to compensate for.

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 uses a specific verb (列出/list) with a concrete resource: configured news sources, hot list sources, supported categories, and last run status. Enumerating exactly what it exposes clearly distinguishes it from sibling tools that search, aggregate, or fetch individual news articles rather than listing configuration.

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 on when to use this tool versus the six siblings. Its plausible role as a discovery/preflight step before calling search_china_news or aggregate_china_news must be inferred by the agent; nothing states 'call this first to see available sources and categories' or identifies conditions that make it unnecessary.

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?

注解已经声明 readOnlyHint=true 和 openWorldHint=true，描述额外补充了输出会被规范化、去重且可引用，这些是有价值的行为信息。未提及分页、速率限制或具体返回结构，但在已有注解覆盖下仍算充分。

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?

一句话概括了核心查询维度和输出特性，信息密度高且没有冗余。但所有内容挤在一个句子中，缺少分段或列举结构，可读性略受影响。

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?

工具包含 7 个参数且没有输出 schema 或参数描述，描述虽然点明了主要查询维度，但未说明返回结果的具体结构、排序含义、时间范围默认值、source_ids 用法或与其他聚合/搜索工具的选择关系。对于这样一个中等复杂度的搜索工具，信息不足以让智能体完全正确调用。

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 中参数没有任何描述（覆盖率为 0%），描述通过“关键词、分类、来源和时间范围”间接映射了 query、category、source_ids、from/to，但对 sort、limit 的语义没有说明，也没有解释各枚举值的具体含义。描述部分补偿了 schema 的不足，但不够完整。

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?

描述使用明确的动词“查询”和资源“中国相关中文新闻”，并列出关键词、分类、来源、时间范围等查询维度。与 search_international_news 等兄弟工具在范围上形成清晰区分，且说明输出为规范化、去重、可引用的结果。

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?

描述隐含了按多种条件检索中国新闻的用途，但没有明确说明何时应使用本工具而不是 aggregate_china_news 或 search_international_news。没有提供 when/when-not 或替代工具的提示。

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?

Annotations provide readOnlyHint and openWorldHint, so the safety/read-only burden is covered. The description adds useful behavior beyond the schema: supported sources, bilingual results, and normalized/deduplicated/citable output.

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 dense sentence with no filler; the main action, source scope, language support, and output qualities are all 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?

For an 8-parameter tool with no output schema and zero schema descriptions, this one-sentence description leaves too much unspecified: date-range format, sort behavior, category filtering, and result structure are absent.

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 0% and the description compensates only minimally—it maps the language concept to en/zh but never explains to/from, sort, limit, category, or source_ids semantics. Parameter names and enums remain the only clues.

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 '查询' and resource 'international sources' with examples (NPR, France 24, Sky News, GDELT), and notes language support. It distinguishes from search_china_news by source scope, but does not clearly differentiate from aggregate_international_news.

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 explicit when-to-use guidance or exclusions. The international-source framing only implicitly separates it from the China-news siblings, and there is no mention of when to choose aggregate vs search tools.

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