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

Beyond the annotations (readOnlyHint=false, destructiveHint=true), the description clarifies the exact blast radius: it changes only the label, does not delete the row, does not retrain, and takes effect at the next training run. This resolves the ambiguity created by a bare destructiveHint and adds genuine operational 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?

Three short sentences, zero filler, with the core action and the hard limit front-loaded and the side-effect exclusions following immediately. Every sentence carries distinct 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 small mutation tool with no output schema but with annotations covering its safety profile, the description covers effects, timing, and limits well. It is silent only on return value and any permission requirements, which is a minor gap.

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 50% (model_id and the nested 'text' field are documented; category and row_index are not). The description adds little about individual parameters other than restating the 50-item cap already encoded as maxItems. Baseline 3 is appropriate since the schema does the bulk of the work.

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 verb+resource (change the category of mislabelled training rows) and bounds the scope (up to 50), so the agent knows exactly what operation this performs. It does not explicitly differentiate itself from near-neighbours like suggest_labels or apply_quality_choices, so it stops short of a 5.

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 'rows the user agreed are mislabelled' implies the tool is used after a review/agreement step, which is useful implicit context. There is no explicit when-to-use/when-not statement and no named alternative (e.g. suggest_labels), so guidance is only implied.

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