get_dupont_data

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool 'fetches' data and returns a markdown table or error, but does not cover critical aspects like authentication needs, rate limits, data freshness, or error handling specifics. This is a significant gap for a data-fetching tool with no annotation support.

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, with a clear purpose statement followed by structured sections for Args and Returns. Every sentence earns its place, providing essential information without waste, making it easy to scan and understand.

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 mostly complete. It covers the purpose, parameters, and return format. However, it lacks behavioral context like error conditions or usage guidelines, which would be needed for a perfect score, especially without annotations.

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 clearly explains each parameter: 'code' as the stock code with an example, 'year' as the 4-digit year with an example, and 'quarter' as the quarter with valid values. This fully compensates for the schema's lack of descriptions.

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 DuPont analysis data (ROE decomposition) for a stock,' specifying the verb (fetches), resource (DuPont analysis data), and scope (quarterly, for a stock). However, it does not explicitly differentiate from sibling tools like 'get_hk_dupont_data' or other data-fetching tools, which would require a 5.

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 'get_hk_dupont_data' for Hong Kong stocks or other financial data tools in the sibling list. It lacks explicit when/when-not instructions or named alternatives, offering only implied usage through parameter context.

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