analyze_topic_trend

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

No annotations exist, so the description carries the full burden. It discloses the JSON return format, the default date range, and mode-specific parameter behavior (viral thresholds, prediction lookahead, confidence). It does not mention rate limits, auth, or side effects, but for an analysis tool this 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-organized with a title line, a usage tip, a compact Args block, Returns, and Examples. Every section adds value and the format is easy for an agent to scan.

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 8 parameters, 0% schema description coverage, no annotations, and an output schema, the description is largely complete: it explains parameter semantics, defaults, mode-specific options, and gives examples. Minor gaps remain, such as valid granularity values and explicit sibling-tool selection guidance.

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%, yet the description documents every parameter: topic as required, analysis_type with its four accepted values, date_range format, granularity, spike_threshold, time_window, lookahead_hours, and confidence_threshold. It fully compensates for the schema's lack of descriptions.

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 it as a topic-trend analysis tool ('统一话题趋势分析工具') and enumerates four analysis modes. It states a specific verb and resource, but it does not explicitly contrast it with sibling analysis tools like analyze_data_insights or analyze_sentiment.

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?

It gives one practical workflow hint: call resolve_date_range for natural-language dates. However, it does not say when to prefer this tool over its analysis siblings or when not to use it, so the usage guidance is implied rather than explicit.

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