analyze_topic_trend

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

With no annotations, the description carries the full burden of disclosing behavior. It states that it returns JSON and documents mode-specific parameters, but it does not explicitly state whether the tool is read-only, whether it has side effects, requires authentication, or how it handles invalid or missing data. The term 'analysis' implies non-mutating, but that remains implicit rather than explicit.

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: a brief summary, a practical usage tip, a clear Args list with inline explanations, and two illustrative examples. Although somewhat long, every section earns its place, and the main 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 an 8-parameter tool with no annotations and a minimal schema, the description covers all parameters, mode semantics, and even provides a cross-tool dependency hint. It does not discuss data sources, performance, or rate limits, but the presence of an output schema and the detailed parameter guidance make the tool sufficiently usable.

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?

The input schema has 0% description coverage, providing only types and defaults. The description fully compensates by explaining every parameter, including the valid values for analysis_type, the expected format for date_range, and the mode-specific meanings of spike_threshold, time_window, lookahead_hours, and confidence_threshold. This is exemplary parameter documentation.

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), which distinguishes it from sibling tools like analyze_sentiment or get_trending_topics. It lacks an explicit imperative verb like 'analyzes', but the name and detailed mode list make the purpose unambiguous.

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 explicitly advises calling resolve_date_range when using natural-language dates, which is a clear cross-tool usage guideline. It also documents each analysis_type mode with defaults, helping the agent choose the appropriate mode. However, it does not explicitly say when to prefer this over sibling tools or 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.