riegel_predict_race_time

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. While it mentions the formula used, it doesn't describe important behavioral aspects: whether this is a read-only calculation (implied but not stated), what assumptions Riegel's formula makes, accuracy limitations, or error handling. For a prediction tool with zero annotation coverage, this is inadequate.

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 perfectly structured and concise. It opens with the core purpose, mentions the method, provides clear parameter documentation, and describes the return format. Every sentence earns its place with no wasted words, and information is logically organized from general to specific.

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 (mathematical prediction), 0% schema coverage, no annotations, but presence of an output schema, the description does well. It explains parameters thoroughly and mentions the return format. The main gap is lack of formula limitations/assumptions context. With an output schema handling return values, the description focuses appropriately on inputs and purpose.

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 provides clear parameter documentation in the Args section, explaining what each parameter represents (current_distance in meters, current_time in seconds, target_distance in meters). With 0% schema description coverage, this documentation fully compensates by adding essential semantic meaning beyond the bare schema types. The only minor gap is not explaining unit expectations beyond 'meters' and 'seconds'.

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 method ('Uses Riegel's formula'). However, it doesn't explicitly differentiate from sibling tools like 'daniels_predict_race_time' or 'mcmillan_predict_race_times', 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. With multiple prediction tools available (Riegel, Daniels, McMillan), there's no indication of when Riegel's formula is preferred, what its limitations are, or when other methods might be more appropriate. This leaves the agent without usage context.

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