Sibyl

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context (e.g., 'real search interest over time', details on what data is shown) but lacks information on rate limits, authentication needs, error handling, or data freshness. It describes output content but not format or structure, which is partially covered by the output schema.

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 appropriately sized and front-loaded, starting with the core purpose. The Args section is well-structured but slightly verbose; every sentence adds value (e.g., clarifying parameters), though it could be more streamlined by integrating examples into the main text without redundancy.

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 moderate complexity (2 parameters, no annotations, but with an output schema), the description is fairly complete. It covers purpose, parameters, and output content, and the output schema likely handles return values. However, it lacks usage guidelines and some behavioral details (e.g., rate limits), leaving minor gaps for the agent.

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. It adds meaningful semantics: 'keywords' is explained as comma-separated with a max of 5 and an example, and 'timeframe' is detailed with specific options and a default. This goes beyond the bare schema, providing practical usage context, though it could include more on validation or constraints.

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's purpose with specific verbs ('Get Google Trends data') and resources ('keywords'), distinguishing it from siblings like 'analyze', 'chart', or 'fetch_market_data' by focusing on real search interest over time. It specifies what data is retrieved (interest level, trend direction, peak, rising related searches), making the purpose explicit and differentiated.

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 no guidance on when to use this tool versus alternatives like 'analyze', 'chart', or 'compare', which might overlap in functionality. It mentions the tool's scope (Google Trends data) but does not specify use cases, exclusions, or comparisons with siblings, leaving the agent to infer usage from context alone.

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