get_trending_topics

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

With no annotations, the description carries the behavioral disclosure burden and does well: it defines 'daily' versus 'current' modes, explains the difference between preset keyword extraction and auto-extraction, mentions the config file dependency, and notes that auto_extract requires no preset. It could add explicit read-only/no-side-effect language, but the semantics are well covered.

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 well-structured: a one-line summary, clear Args list, Returns statement, and concrete examples. Every section adds useful information, and the most important behavioral choices (mode and extract_mode) are 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?

Given the output schema exists, the return values need minimal description, and the Args/Examples fully explain the tool's expected inputs and modes. It is slightly incomplete in that it doesn't clarify how 'trending' is computed beyond frequency counts or how it relates to sibling analytics tools, but overall it gives an agent enough to call it correctly.

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 is the only parameter documentation. All three parameters (top_n, mode, extract_mode) are explained with their defaults and allowed values, and examples clarify realistic combinations. This fully compensates for the schema's lack of descriptions.

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 summary '获取热点话题统计' clearly identifies the tool as retrieving trending/topic frequency statistics, and the detailed Args/Examples make the resource and actions unambiguous. It does not explicitly distinguish itself from sibling tools like analyze_topic_trend or aggregate_news, but the name and description make the core purpose clear.

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 explains modes and extract modes but provides no guidance about when to choose this tool over alternatives such as analyze_topic_trend, get_latest_news, or aggregate_news. Examples show common invocations, but there is no explicit when-to-use or when-not-to-use guidance.

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