analyze_topic_trend

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

With no annotations provided, the description carries full burden. It describes the tool's multi-mode behavior and mentions JSON output format, but lacks critical behavioral details: whether this is a read-only operation, computational cost, rate limits, authentication requirements, or what happens with invalid parameters. The description adds some behavioral context but leaves significant gaps for a tool with 8 parameters.

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 clear sections (overview, advice, args, returns, examples) and uses bullet points effectively. While comprehensive, some sentences could be more concise (e.g., the opening line could be tighter). Overall, it's appropriately sized for an 8-parameter tool with multiple modes.

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, multiple analysis modes, no annotations) and the presence of an output schema (which covers return values), the description is reasonably complete. It explains parameters thoroughly, provides usage guidance, and includes examples. The main gap is lack of behavioral transparency details that would be important for a multi-mode analysis tool.

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?

With 0% schema description coverage, the description compensates excellently by providing detailed parameter semantics beyond the bare schema. It explains each parameter's purpose, lists analysis_type options with descriptions, specifies defaults, clarifies which parameters apply to which modes, and provides format examples. This adds substantial value beyond the minimal 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 states the tool performs 'topic trend analysis' with 'integrated trend analysis modes' and lists specific analysis types (trend, lifecycle, viral, predict). It distinguishes from siblings like 'get_trending_topics' by focusing on analysis rather than listing. However, it doesn't explicitly differentiate from 'analyze_data_insights' or 'compare_periods' which might have overlapping functionality.

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 explicit guidance to call 'resolve_date_range' first when using natural language dates, which is helpful context. It also implies usage through the analysis_type parameter options, suggesting when to use different modes. However, it doesn't explicitly state when NOT to use this tool or mention alternatives among siblings like 'analyze_data_insights'.

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