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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds meaningful context beyond that by explicitly stating the result is estimated model output rather than observed telemetry, and it exposes key return fields like residual_std and sample_count, which signal reliability and uncertainty.

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 tightly organized with a one-sentence summary followed by compact Args and Returns sections. Every line provides necessary information, and the key behavioral note about estimated output is prominently placed at the end.

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?

For a three-parameter model-fitting tool with an output schema and clear return fields, the description is largely complete. It covers inputs, output shape, and the estimated nature of the result; a minor gap is the absence of caveats about data sufficiency or when model fitting might fail.

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 100%, and the description simply restates the same parameter meanings (session key, driver number, compound) without adding new detail such as formats, constraints, or compound-specific behavior. This meets the baseline for schema-covered parameters but adds no extra semantic value.

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 opens with a specific verb and resource: 'Fit a tyre degradation model for a driver + compound in a session.' It clearly identifies the exact inputs and the statistical nature of the tool, distinguishing it from raw-telemetry sibling tools like f1_get_lap_times.

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?

No explicit when-to-use or when-not-to-use guidance is provided relative to sibling tools. The 'model output, not telemetry oracle' phrase hints at a distinction from raw data tools, but it does not state when an agent should choose this over alternatives like f1_race_pace_compare or f1_predict_pit_strategy.

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