analyze_topic_trend

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

With no annotations, the description is responsible for behavioral disclosure. It adds context about default date range (7 days), mode-specific parameters (spike_threshold for viral, lookahead_hours for predict), and a dependency on resolve_date_range for date processing. However, it does not mention potential side effects, data freshness requirements, or performance implications.

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 title, usage suggestion, parameter list, return note, and two examples. It is moderately concise and front-loaded with the tool's purpose. The parameter list is dense but clear, and every section earns its place.

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 (8 params, no annotations), the description covers the full parameter space, usage patterns, and return format (JSON). It also provides two representative examples. Since an output schema exists, detailed return field explanations are unnecessary. Minor gap: no mention of error conditions or data availability prerequisites.

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 zero description coverage, so the description carries the full burden. It explains all 8 parameters, including the exact string format for date_range, the enum-like values for analysis_type with their purposes, and mode-specific defaults for spike_threshold, time_window, lookahead_hours, and confidence_threshold. This thoroughly compensates for the schema gap.

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 as '统一话题趋势分析工具' (unified topic trend analysis tool) with explicit analysis modes: trend, lifecycle, viral, and predict. It specifies the action (analyze), resource (topic trend), and distinguishes from sibling tools like analyze_sentiment or analyze_data_insights by focusing on trend-specific patterns.

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?

Provides a practical usage tip to call resolve_date_range for natural language dates, and explains when each analysis_type is appropriate (e.g., viral for anomaly detection, predict for forecasting). However, it does not explicitly contrast with sibling tools like analyze_data_insights or suggest when not to use this tool.

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