analyze_topic_trend

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

With no annotations, the description carries full behavioral burden. It explains the effect of each analysis_type mode, documents default values, and specifies the return format ('JSON格式的趋势分析结果'). It also warns about date handling. It could disclose more about side effects or data sourcing, but it adds significant behavioral context beyond the bare-bones schema.

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 suggestion header, a parameter list, return type, and examples. It is slightly long, but that is necessary to explain 8 parameters in the absence of schema descriptions. Each section adds value and the purpose is front-loaded.

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?

For a tool with 8 parameters and 4 distinct modes, the description is notably complete. It covers all parameters, their defaults, mode-specific usage, and provides two concrete examples. It even flags the date-range helper. The output schema exists, so return details are not required, but the description still mentions JSON format.

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%, so the description must compensate, and it does comprehensively. It explains each parameter's meaning, format (e.g., date_range as {'start': 'YYYY-MM-DD', 'end': ...}), and mode-specific applicability (e.g., spike_threshold for viral mode, lookahead_hours for predict). Examples further clarify 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 clearly states the tool's purpose: '统一话题趋势分析工具' (unified topic trend analysis tool) and lists four specific modes (trend, lifecycle, viral, predict). It identifies the resource (topic) and action (analyze), and the modes distinguish its scope from any sibling analysis tools.

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 a concrete prerequisite: 'use resolve_date_range for natural language dates' before calling this tool. It also differentiates internal modes (e.g., viral, predict) with clear parameter triggers. However, it does not explicitly state when to use this tool versus alternatives (e.g., analyze_sentiment, analyze_data_insights), nor does it state when not to use it.

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