get_trending_topics

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

With no annotations, the description carries the full burden for behavioral transparency. It discloses the difference between daily and current modes, the dependency on a config file for keywords mode, the automatic title extraction for auto_extract mode, and the JSON return format. It does not mention any side effects or read-only status, but no such behavior is implied, so it is reasonably transparent.

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 uses bullet points for enums. Every sentence contributes value, and the examples are concise yet illustrative. No redundant or filler content.

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 complexity of three parameters with distinct options and an output schema, the description fully explains all inputs, outputs, and provides examples. It also covers default values and the intent of each mode, making it complete for an agent to select and invoke the tool correctly.

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 schema provides parameter names and defaults but no descriptions (coverage 0%). The description compensates fully by explaining each parameter in detail: top_n (number of topics), mode (daily vs current with definitions), and extract_mode (keywords based on config file vs auto_extract from news titles). This is a complete semantic mapping.

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 obtains trending topic statistics (获取热点话题统计) and specifies the modes and extraction options, distinguishing it from sibling tools like analyze_topic_trend which focus on trend analysis. It is specific about the resource (hot topics) and the verb (get statistics).

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 each mode (daily vs current) and extraction method (keywords vs auto_extract), with practical examples. However, it does not explicitly mention alternatives or when not to use this tool versus other sibling tools, so it stops short of a 5.

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