analyze_topic_trend

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

With no annotations provided, the description carries the behavioral transparency burden and does it well by explaining the four analysis modes, mode-specific parameters, and defaults such as spike_threshold, time_window, lookahead_hours, and confidence_threshold. It also states the return type as 'JSON格式的趋势分析结果', giving the agent a clear picture of the tool's behavior.

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 a one-line summary, an actionable suggestion, a parameter list, return type, and examples. It is appropriately sized for an 8-parameter tool with multiple modes, and every section adds useful information without redundancy.

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's complexity (8 parameters, 4 analysis modes, no annotations), the description is sufficiently complete: all parameters are explained, defaults are given, mode-specific parameters are called out, and concrete examples show real invocation patterns. An output schema exists, so the brief return description is acceptable.

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 0% description coverage, so the description must fully document the parameters, and it does: topic, analysis_type with all four allowed values, date_range format, granularity, spike_threshold, time_window, lookahead_hours, and confidence_threshold are all explained with defaults and mode relevance. Examples further clarify parameter usage.

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 opens with '统一话题趋势分析工具 - 整合多种趋势分析模式', clearly identifying the tool as a topic trend analysis utility with multiple analysis modes. It distinguishes itself from siblings like analyze_sentiment or compare_periods by specifying trend, lifecycle, viral detection, and prediction as its core functions.

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 explicitly advises calling resolve_date_range first when using natural language dates, which is a clear usage guideline tied to a sibling tool. It also documents the default behavior ('默认最近7天', '默认"day"'), but does not explicitly state when to prefer this tool over alternatives such as get_trending_topics or analyze_data_insights.

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