get_trending_topics

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

No annotations are provided, so the description takes on the burden of explaining behavior: it details what is computed in each mode, where keywords come from, and what the return value looks like. It does not mention potential side effects or whether the dataset is automatically refreshed, but a read-only behavior is strongly implied.

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 using a docstring-style layout with Args, Returns, and Examples. Every sentence contributes useful information, and examples summarize the main use cases concisely.

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?

For a tool with three optional parameters, no annotations, and an output schema, the description is sufficiently complete. It covers all parameters, return format, and example usage scenarios.

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, but the description fully compensates by explaining all three parameters, their defaults, allowed modes, and real usage examples. This leaves no ambiguity about how to set each parameter.

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 identifies the tool's function: generating counts and statistics on hot topics, which matches the name 'get_trending_topics'. It is distinct enough from generic news-fetching tools, though it does not explicitly distinguish itself from the overlapping sibling tool 'analyze_topic_trend'.

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 gives strong internal usage guidance, such as the 'daily' vs 'current' modes and 'keywords' vs 'auto_extract' modes, along with concrete examples. However, it does not mention when to choose this tool over related siblings like 'analyze_topic_trend' or 'get_latest_news'.

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