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

Beyond annotations (readOnly, idempotent), the description discloses that predictions are point estimates without confidence intervals and flags the optimistic ceiling for marathon-from-short-race predictions. It also reveals the underlying algorithm and the effect of useMyData, adding meaningful behavioral context.

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 compact and front-loaded with the core function, then adds algorithm details, limitations, source, and usage tip in a logical sequence. Every sentence adds unique value with no filler.

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 output schema and annotations, the description adequately covers the algorithm, limitations, and optional profile overlay. It lacks an explicit statement of when not to use the tool, but overall it is sufficiently complete for a read-only prediction utility.

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 input schema already provides 100% parameter coverage, so the description does not need to repeat definitions. It adds a helpful hint about useMyData overlaying profile data and clarifies the distance scope, but the schema carries the primary semantic load.

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 predicts race times across 5K/10K/Half/Marathon from a known race result using a specific algorithm. This specific verb+resource combination distinguishes it from siblings like pacing_strategy or generate_training_plan.

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?

It specifies the core use case: input a known race result to get predictions. It also provides a caution about interpreting marathon predictions from short races, implying when to be careful. However, it does not explicitly name alternative tools or scenarios where this tool should not be used.

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