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

The annotations already state readOnlyHint=false, openWorldHint=true, idempotentHint=false, and destructiveHint=false, so the safety profile is largely covered. The description is consistent with those annotations but adds little about what happens to a submitted report, whether it is reviewed, or what confirmation is returned.

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 two tight sentences, front-loads the core purpose, and uses examples efficiently. There is no wasted text.

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 four-parameter reporting tool with no output schema and no parameter descriptions, the description is too sparse. It explains the kind of problem to report but leaves the required inputs and the outcome of reporting unclear.

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 0%, so the description must explain the four parameters. It only hints that quote and source_url are optional supporting evidence and does not explain what note should contain or what slug identifies.

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 states a specific action, reporting a problem, and clarifies scope with examples such as cancelled races, duplicate rows, and wrong distance. This distinguishes it from a simple single-value correction, though it does not explicitly name the sibling propose_correction.

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 phrase 'something a single value cannot fix' gives a clear usage boundary, and the examples make the intended cases concrete. It does not explicitly say when to use propose_correction instead, but the contrast is strongly implied.

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