Get Race Predictions

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe, non-destructive operation. The description adds the meaningful context that results are derived from current fitness data. It does not contradict annotations and provides value beyond the structured metadata.

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 a single, concise sentence with no filler. It front-loads the action and resource, and every word adds meaning. It is optimally sized for the tool's simplicity.

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 no parameters and no output schema, the description adequately conveys what it does and what it depends on (current fitness). It could mention the returned format or that data must exist, but for a straightforward prediction tool, this level of detail is sufficient and comparable to high-scoring examples.

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?

With zero parameters, the schema is fully covered (100%). The description adds no parameter-specific information, but none is needed. According to the calibration, a baseline of 4 is appropriate for tools with no parameters.

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: 'Get predicted race times for 5K, 10K, half marathon, and marathon based on current fitness.' It specifies a specific verb (get), a precise resource (predicted race times for four distances), and the basis (current fitness). This distinguishes it from siblings like get_personal_records or get_fitness_age, which focus on actual data rather than predictions.

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 mentioning 'based on current fitness,' which suggests it should be used when the user wants an estimate of their race performance. However, it does not explicitly state when not to use it or name alternatives. Since no sibling tool provides predictions, the lack of explicit exclusions is acceptable.

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