analyze_topic_trend

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

With no annotations, the description carries the transparency burden. It discloses the tool's multi-mode behavior, JSON return format, and date-range resolution prerequisite. It does not mention permissions, side effects, error behavior, or result structure, but the read-only nature of an analysis tool is reasonably inferable.

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 brief summary, a conditional recommendation, parameter definitions, return type, and examples. Each section earns its place and key information 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?

Given eight parameters, no annotations, and an output schema, the description covers all parameter semantics, the return format, and a date-resolution prerequisite, with examples. It could add result interpretation or error-handling details, but the existing explanation and output schema make it sufficiently complete.

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%, and the description fully compensates by documenting all eight parameters, including analysis_type choices, mode-specific thresholds, defaults, and examples. This adds rich meaning beyond the bare input 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 identifies the tool as a unified topic trend analysis tool integrating multiple analysis modes (trend, lifecycle, viral, predict). It states a specific resource and action, but does not explicitly differentiate it from sibling analysis tools like analyze_sentiment or analyze_data_insights.

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 provides one explicit usage recommendation: call resolve_date_range first when using natural-language dates. However, it does not state when to prefer this tool over sibling analysis tools or when not to use it, so alternative selection guidance is mostly implied by the analysis_type modes.

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