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 mentions the reliance on config/frequency_words.txt for keyword mode and describes the return format, which is useful. However, it never explicitly states that this is a read-only operation, nor does it mention any side effects, permissions, or data freshness implications, leaving room for ambiguity.

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 an opening summary, Args block, Returns block, and Examples. Each section is purposeful and adds distinct value, with no redundant filler or repetition of schema defaults. The length is appropriate for the parameter complexity.

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 that an output schema exists (so return details are covered) and the description thoroughly documents all parameters with usage examples and config file references, the tool is adequately specified. An agent can confidently invoke the tool with correct parameters and understand the expected output format.

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 has no parameter descriptions (0% coverage), but the description's Args section fully compensates by explaining every parameter with defaults and meaning. It defines 'top_n', both values of 'mode', and both values of 'extract_mode', providing substantially more semantic value than the raw 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 as retrieving trending topic statistics ('获取热点话题统计'), and the Args details clarify the two time modes and two extraction modes that shape the output. However, it does not explicitly differentiate itself from sibling tools like analyze_topic_trend or analyze_data_insights, so it is specific but not fully distinguished from alternatives.

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 guidance on when to use each mode, especially distinguishing between 'daily' vs 'current' and 'keywords' vs 'auto_extract'. The examples offer practical invocation patterns. It does not, however, state when to prefer this tool over sibling tools, so it stops short of explicit alternative-based guidance.

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