get_growth_data

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 mentions the return format ('Markdown table with growth capability data or an error message'), which adds some context, but fails to cover critical aspects like data sources, rate limits, authentication needs, error handling specifics, or whether the operation is read-only or has side effects. This leaves significant gaps for an agent to understand the tool's behavior fully.

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 and appropriately sized, with a clear purpose statement followed by 'Args' and 'Returns' sections. Each sentence adds value, such as specifying data types and examples, without unnecessary fluff. However, it could be slightly more front-loaded by integrating the return format into the initial sentence for faster comprehension.

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 (3 parameters, no annotations, but with an output schema), the description is partially complete. It excels in parameter semantics and purpose clarity but lacks usage guidelines and sufficient behavioral transparency (e.g., no info on data freshness or error conditions). The presence of an output schema reduces the need to detail return values, but overall gaps remain in guiding effective tool selection and 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?

The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explicitly defines each parameter: 'code' as 'The stock code (e.g., 'sh.600000'),' 'year' as 'The 4-digit year (e.g., '2023'),' and 'quarter' as 'The quarter (1, 2, 3, or 4).' This includes examples and constraints, fully compensating for the schema's lack of documentation and ensuring clarity for an agent.

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 'fetches quarterly growth capability data (e.g., YOY growth rates) for a stock,' specifying the verb ('fetches'), resource ('growth capability data'), and scope ('quarterly,' 'for a stock'). However, it does not explicitly differentiate from sibling tools like 'get_hk_growth_data' or 'get_profit_data,' which may also fetch financial data, leaving some ambiguity about its unique role.

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, such as sibling tools like 'get_hk_growth_data' for Hong Kong stocks or 'get_profit_data' for other financial metrics. It lacks context on prerequisites, exclusions, or comparisons, offering only basic parameter usage without strategic direction.

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