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

Beyond the annotations (readOnly=false, destructive=false, openWorld=false), the description discloses the dry-run-first confirmation flow, that the first call changes nothing, and that it returns allowance usage plus a confirmation code. This is exactly the behavioral context an agent needs before triggering a mutation.

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 tight sentences, front-loaded with purpose, then the two-step mechanics, then the boundary with publish. No filler and every clause carries operational meaning.

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?

No output schema exists, yet the description explains what the first call returns (projected additions, allowance consumption, confirmation code), which is the key unknown. Combined with the 100% schema coverage and annotations, nothing an agent needs to invoke this correctly is missing.

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%, so both parameters are already documented, including the 'confirmation' code semantics. The description reinforces the ordering (only send the code after explicit user agreement) but adds little syntax or format detail beyond the schema.

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 and resource ('Retrain the model') plus the exact data source (answers reviewed in the Review Queue) and the settings baseline (live version). This clearly separates it from siblings like train, publish, and review_answer.

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?

Gives an explicit two-step protocol: first call is a no-op dry run, second call requires the exact confirmation code and explicit user agreement. It also routes publishing to the 'publish' sibling, so an agent knows where this tool's responsibility ends.

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