analyze_topic_trend

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

With no annotations, the description fully discloses behavioral traits: it states that granularity only supports 'day' due to data aggregation, that date_range must be in YYYY-MM-DD format (no natural language), and the return format is JSON. No contradictions.

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 structured with sections, bullet points, and examples, making it easy to parse. It is slightly lengthy but every sentence contributes meaningful guidance. Could be slightly more streamlined but still effective.

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 output schema exists, the description does not need to detail return values. It covers all 8 parameters, usage patterns, edge cases (like calculating dates), and includes multiple examples. It is complete for an AI agent to 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?

Schema description coverage is 0%, but the description thoroughly explains each parameter: analysis_type with all four values and their meanings, date_range format and calculation rules, granularity constraint, threshold, time_window, lookahead_hours, confidence_threshold. It adds significant value beyond the bare 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 '统一话题趋势分析工具' (unified topic trend analysis tool) and lists multiple analysis modes (trend, lifecycle, viral, predict), distinguishing it from sibling tools like analyze_sentiment or get_trending_topics.

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 explicit guidance on when to use each analysis_type, and crucially instructs the AI to calculate date_range from relative time expressions using the current date, with examples. This goes beyond basic usage to handle a common user input pattern.

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