mcmillan_predict_race_times

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

No annotations are provided, so the description carries the full burden. It mentions the methodology ('McMillan') and output format ('HH:MM:SS'), which adds useful context beyond basic functionality. However, it lacks details on behavioral traits such as error handling, assumptions (e.g., standard distances), or limitations (e.g., accuracy range).

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: the first sentence states the core purpose, followed by structured 'Args' and 'Returns' sections. Every sentence earns its place by adding essential information 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 mostly complete. It covers purpose, inputs, and output format. The output schema exists, so explaining return values is unnecessary. However, it could benefit from more behavioral context (e.g., methodology details or limitations).

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 meaning by specifying units ('meters' for distance, 'seconds' for time) and clarifying that inputs are based on a 'single race performance,' which is not evident from the schema alone. However, it does not detail what 'standard distances' entail or provide examples.

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: 'Predict race times for standard distances based on a single race performance using McMillan methodology.' It specifies the verb ('predict'), resource ('race times'), scope ('standard distances'), and methodology ('McMillan'), distinguishing it from sibling tools like 'riegel_predict_race_time' or 'daniels_predict_race_time'.

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 clear context for when to use this tool: when predicting race times based on a single performance using the McMillan method. It implicitly distinguishes it from other prediction methods (e.g., Riegel, Daniels) by naming the methodology, but it does not explicitly state when not to use it or list specific alternatives among siblings.

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