analyze_topic_trend

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

With no annotations, the description carries the behavioral disclosure burden. It discloses useful defaults like date_range defaulting to recent 7 days and granularity to 'day', plus mode-specific thresholds. However, it does not mention edge-case behavior, error conditions, data source assumptions, or whether some parameters are ignored in certain modes beyond what is implied.

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 an overview, prerequisite suggestion, Args block, Returns line, and Examples. Each parameter description earns its place. It is longer than minimal, but the detail is necessary because the schema provides no property descriptions and the tool has 8 parameters.

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 and 0% schema coverage, the description is largely complete: it explains all parameter semantics, defaults, mode-specific use, example calls, and a cross-tool prerequisite. It could be more complete by enumerating valid granularity values or describing behavior when optional parameters are irrelevant, but overall it is sufficient for accurate invocation.

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 entirely for parameter meaning. It fully explains every parameter, provides valid values for analysis_type, gives a concrete date_range format, specifies defaults, and gives correct use examples. This is far more useful than the schema alone.

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 explicitly states it is a unified topic trend analysis tool and lists concrete analysis modes: trend, lifecycle, viral, and predict. This clearly distinguishes it from sibling tools like get_trending_topics, analyze_sentiment, or resolve_date_range.

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 explicit workflow guidance: when using natural language dates, first call resolve_date_range. It also defines mode-specific parameters, telling the agent when spike_threshold and time_window apply vs lookahead_hours and confidence_threshold. It lacks explicit when-not-to-use statements for other siblings.

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