Day-count fraction

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

The description calls the tool 'exact' and 'deterministic', which covers behavioral traits well in the absence of annotations. However, it does not disclose what the output looks like (e.g., a floating-point number), nor does it mention any edge cases or error handling, leaving some behavioral details implicit.

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 two sentences long and front-loads the core purpose. Every word is necessary; no filler or redundancy exists.

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 has 4 parameters (including an optional boolean) and no output schema, the description is insufficiently complete. It does not explain the return format, precision, error behavior (e.g., invalid dates, start after end), or provide examples, leaving agents without enough context for correct usage.

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 75%, and the description repeats information already in the schema (e.g., 'Dates are ISO YYYY-MM-DD'). It adds no new meaning beyond what the input schema provides, so the baseline score of 3 is appropriate.

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 explicitly states the verb 'compute day-count fraction between two dates under a market convention', which is specific and unambiguous. The sibling tools 'accrued_interest' and 'list_conventions' are clearly distinct in purpose, so no confusion arises.

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 advises against using a model to estimate the fraction ('Deterministic; do not estimate this with a model'), implying that this tool should be used for exact calculations. However, it does not explicitly state when to use this tool over alternatives, nor does it provide context about prerequisites or conditions.

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