get_trending_topics

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

No annotations are provided, so the description carries full responsibility for disclosing behavior. It does reveal that it reads from 'config/frequency_words.txt' for the 'keywords' mode and can automatically extract high-frequency terms from news titles, implying read-only statistical computation. However, it does not explicitly state that the operation is side-effect-free, whether it triggers network calls, or if there are any latency or resource implications. This leaves some ambiguity for a tool with zero annotation coverage.

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 structured with clear sections (Args, Returns, Examples) and is relatively compact. The purpose is front-loaded, and parameter explanations are concise but complete. It could be slightly shortened by removing some redundancy (e.g., repeating defaults), but overall it is efficient and well-organized.

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 tool's simplicity (three optional parameters, no required fields), the description covers all essential aspects: parameter semantics, return format (JSON list of topic frequencies), and practical examples. It does not mention any side effects, rate limits, or data freshness, but the provided details are sufficient for a basic read-only statistics tool. The presence of an output schema (not shown here but indicated) means return details are partly covered externally, so the description is adequately 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?

The input schema provides only names and defaults, with no descriptions (0% coverage). The description compensates fully: it explains each parameter's meaning, lists and defines acceptable values (e.g., 'daily' and 'current' for mode, 'keywords' and 'auto_extract' for extract_mode), and clarifies the purpose of top_n. This is far more than the schema offers, making it easy for an agent to invoke correctly.

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 opens with a clear, specific phrase '获取热点话题统计' (get trending topic statistics), which names the verb and resource precisely. It distinguishes this tool from siblings like 'analyze_topic_trend' by focusing on frequency statistics rather than trend analysis, so an agent can tell it apart without deeper inspection.

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 provides examples of usage and explains parameter modes (e.g., 'daily' vs 'current', 'keywords' vs 'auto_extract'), which implicitly convey when to use different options. However, it gives no explicit guidance on when to select this tool over related siblings like 'analyze_topic_trend' or 'search_news', nor does it state any exclusions or prerequisites. The examples show usage but not contextual selection criteria.

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