get_trending_topics

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by explaining the two extraction modes (keywords from config file vs auto-extraction from news titles) and the time modes (daily cumulative vs current batch). However, it doesn't mention potential limitations like rate limits, data freshness, or whether this is a read-only operation (though implied by 'get'), leaving some behavioral aspects unspecified.

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 with clear sections (Args, Returns, Examples) and front-loaded purpose statement. Every sentence earns its place by providing essential information. It could be slightly more concise by integrating some parameter details more tightly, but overall it's efficiently organized with minimal waste.

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 moderate complexity (3 parameters, two operational modes), no annotations, but with an output schema (mentioned as JSON format), the description is quite complete. It covers purpose, all parameters with semantics, return format, and usage examples. The main gap is lack of explicit behavioral constraints (like permissions or limits) that annotations would normally provide, but the output schema reduces the need to describe return values in detail.

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?

With 0% schema description coverage (schema only shows parameter names and types without descriptions), the description fully compensates by providing detailed semantic explanations for all three parameters: top_n (returns TOP N topics with default), mode (explains both daily and current options with meanings), and extract_mode (details both keywords and auto_extract approaches with implementation specifics). This adds substantial value beyond the bare 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 clearly states the tool's purpose with specific verbs ('获取热点话题统计' - get trending topics statistics) and distinguishes it from siblings like analyze_topic_trend or aggregate_news by focusing on frequency-based statistical retrieval rather than analysis or aggregation. It specifies the resource (trending topics) and the statistical nature of the operation.

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 clear context for when to use different modes (daily vs current statistics, keywords vs auto_extract) through examples and parameter explanations. However, it doesn't explicitly state when NOT to use this tool versus alternatives like analyze_topic_trend or when to prefer sibling tools for different analytical needs, missing explicit exclusion criteria.

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