daniels_predict_race_time

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

With no annotations provided, the description carries the full burden. It discloses the methodology ('Jack Daniels' equivalent performance methodology') and return format ('dict: Daniels' VDOT method prediction with value, format, and time_seconds'), but doesn't mention potential limitations, accuracy, or edge cases. It adds some behavioral context but could be more comprehensive for a prediction tool.

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 front-loaded with the core purpose, followed by structured sections for Args and Returns. Every sentence adds value: the first explains what it does, the second the methodology, and the bullet points detail inputs/outputs 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 (3 parameters, prediction logic), no annotations, and an output schema present, the description is fairly complete. It covers purpose, methodology, parameters, and return structure, but could benefit from more behavioral details like error handling or assumptions, though the output schema reduces the need to explain return values.

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 schema description coverage is 0%, so the description must compensate. It adds meaning by explaining each parameter's role ('current_distance: Distance of known performance in meters', etc.), which clarifies beyond the bare schema types. However, it doesn't specify units beyond meters/seconds or validate ranges, leaving some gaps.

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 time for a target distance based on a current race performance.' It specifies the verb ('predict'), resource ('race time'), and methodology ('Jack Daniels' equivalent performance methodology'), distinguishing it from sibling tools like 'riegel_predict_race_time' and 'mcmillan_predict_race_times' by naming the specific method.

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 implies usage context by stating it's for predicting race times based on current performance, but it doesn't explicitly guide when to use this tool versus alternatives like 'mcmillan_predict_race_times' or 'riegel_predict_race_time'. It provides clear input requirements but lacks explicit comparisons or exclusions.

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