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 clearly explains the difference between 'daily' and 'current' modes, and between 'keywords' and 'auto_extract' extraction methods, including that auto_extract automatically discovers hot topics from new headlines. This provides meaningful behavioral context. It does not mention side effects or permissions, but as a read-like operation that is acceptable.

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 Args, Returns, and Examples sections, and it front-loads the main purpose. It is slightly longer than necessary but every section provides value. The examples reinforce parameter usage without being redundant, and the formatting aids readability.

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 a simple interface (3 optional parameters) and the description covers all key aspects: input parameters, return type, and use-case examples. An output schema exists, so detailed return values are already defined. However, it does not mention data availability prerequisites or how this tool relates to sibling trend-analysis tools, leaving minor gaps in overall context.

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 zero description coverage, so the tool description must fully explain the parameters. It does: top_n is described as 'return TOP N topics', mode has enumerated values with definitions, and extract_mode explains both options and the reliance on a config file for 'keywords'. Examples further clarify usage. This fully compensates for the missing schema 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 title '获取热点话题统计' clearly states the tool fetches hot topic statistics. It uses a specific verb (获取/get) and resource (热点话题统计/hot topic statistics), making its primary function obvious. However, it does not explicitly distinguish itself from sibling tools like analyze_topic_trend or analyze_data_insights, which could overlap in purpose.

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 how to configure the tool via parameters (mode, extract_mode) and provides examples, implying its use for retrieving topic frequency counts. However, it does not explicitly state when to prefer this tool over alternatives, nor does it mention exclusions or prerequisites. Usage context is only implicitly conveyed.

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