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

Annotations already declare readOnly, idempotent, and closed-world behavior, so the safety profile needs no restating. The description adds genuinely new behavioral context: that sprints are included, that results are averaged per driver, and that coverage begins in 2023 due to position data availability — a data-availability constraint not expressed anywhere in the schema.

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?

A single sentence that front-loads the unit of analysis ('per driver'), then the metric, then the scope caveat. No redundant restatement of the name or title, and every clause carries information.

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 one-parameter read-only tool with full schema coverage and annotations, the description covers what is computed, over what population, and the data-availability limit. Minor omissions remain, such as whether drivers with no lap-1 data are excluded and how ties or missing positions are handled, but these are unlikely to change how an agent calls it.

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 coverage is 100% and the parameter is documented as a championship year, so the baseline would be 3. The description earns an extra point by narrowing the effective valid range to 2023+ for this metric, which the schema's 1950–2100 bounds do not convey and which directly prevents failed or empty queries.

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?

States a specific computed metric: average places gained or lost on the opening lap, per driver, across races and sprints in a season. The unit of analysis and metric are unambiguous. It does not explicitly distinguish itself from siblings like teammate_head_to_head or peak_finder, but no sibling computes this metric, so confusion risk is low.

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 parenthetical 'seasons with position data: 2023 on' effectively tells the agent when the tool cannot be used, which is real conditional guidance. However, there is no explicit statement of when to prefer this over alternatives or what to do for pre-2023 seasons (e.g., use another tool).

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