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 reveals that the tool depends on a configurable config/frequency_words.txt file, implies a read-only operation by returning a JSON list, and explains the behavioral difference between the two modes. This adds significant context beyond the tool's name, though it stops short of explicitly stating side-effect-free or handling of missing config files.

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-organized with a clear purpose statement, an important clarifying note, and a structured Args/Returns section. While not as terse as possible, every sentence adds value and the layout is easy to scan for an agent. It is appropriately sized for the tool's 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 the tool has only 2 parameters and no annotations, the description is largely complete. It covers the config dependency, mode semantics, and return type, which is sufficient for basic use. It could mention edge cases like an empty config file, but the existence of an output schema mitigates the need to detail the exact return structure. Overall, it provides enough context for correct invocation.

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?

Schema description coverage is 0%, so the description must compensate, and it does comprehensively. The Args section explains top_n as '返回TOP N关注词' with default 10, and mode with two options (daily/current) and their meanings. This fully clarifies parameter semantics that the schema alone does not provide.

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: obtaining news occurrence frequency statistics for personal watched words based on config/frequency_words.txt. It explicitly differentiates itself from automatic news hotspot extraction, which distinguishes it from sibling tools. The verb '获取' and resource '个人关注词的新闻出现频率统计' are specific and unambiguous.

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: it is for statistics on user-defined watched words, not automatic hotspot extraction, which indirectly guides against misuse. It also explains the two modes (daily/current) for selection. However, it does not explicitly name alternative tools, so it stops short of full usage/alternative guidance.

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