analyze_topic_trend

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It explains that it returns JSON results and lists mode-specific parameters, but it doesn't disclose potential behaviors such as rate limits, required authentication, cost implications, or what happens when data is insufficient. For a complex tool with multiple modes, more behavioral transparency (e.g., error handling, data availability dependency) is needed.

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, starting with a clear title and suggestion, then a concise Args list with defaults, a Returns note, and examples. It front-loads the tool's purpose and key usage hint. However, it's slightly long due to detailed parameter listings, but this is justified given the complexity of 8 parameters. The structure is logical and skimmable.

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?

Despite having an output schema (which describes return values), the description provides a brief note that returns are JSON but doesn't detail response fields. For an 8-parameter tool with no enum constraints and no annotations, the description covers parameter semantics, mode-specific behavior, and examples, making it fairly complete. Minor gap: it doesn't clarify how analysis_type interacts with other parameters (e.g., whether date_range is required for 'predict'), but overall it is adequate for an agent to use the tool correctly.

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 schema has 0% description coverage, so the description must compensate. It does an excellent job by detailing each parameter's purpose and, for mode-specific parameters, indicating which analysis_type they apply to. It also provides examples. This goes beyond the schema by explaining the role of each parameter, such as spike_threshold being for 'viral' mode and lookahead_hours for 'predict' mode, which is essential for correct 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 identifies the tool as a unified topic trend analysis tool, with a specific verb ('analyze') and resource ('topic trend'). It distinguishes multiple analysis modes, but doesn't explicitly differentiate from siblings like 'analyze_data_insights' or 'compare_periods'. The purpose is clear and specific enough for an agent to understand its primary function.

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 suggestion to use resolve_date_range for natural language dates, which gives some usage context. However, it doesn't explicitly state when to use this tool versus alternatives, nor does it provide exclusions or conditions for choosing other tools. The guidance is implied through the list of analysis types but lacks explicit when-to-use versus alternatives.

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