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

Annotations already flag this as non-read-only and destructive, but the description adds substantial extra behavior: the first invocation is a no-op, the code is single-use, scoped to this model and these arguments, and expires in 10 minutes. That is real operational context beyond the annotations; it does not, however, say what happens to the previously live version or whether the change is reversible.

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?

Four tightly packed sentences, front-loaded with the effect and immediately followed by the two-step constraint that governs correct invocation. No filler; every clause carries a rule the caller must obey.

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?

With no output schema, the description still tells the agent what the first call returns (the pending changes and a confirmation code). Combined with annotations covering the destructive profile and the description covering the expiry/scope rules, nothing an agent needs to call 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 coverage is 67% and the undocumented parameter is `confirmation`, whose semantics (exact code, single use, model-and-argument scoped, 10-minute expiry) are fully explained in the description. The description thus compensates for the schema gap rather than merely restating job_id/model_id, which the schema already covers.

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 (make live) and resource (a trained version) plus the concrete downstream consumers (fills, classify, Prediction API), which pins down what publishing actually affects. An agent can distinguish it from train, job_status and list_models without reading any schema.

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?

Explicitly prescribes the two-call workflow: first call is a dry run that returns the diff plus a confirmation code, and the second call happens only after the user explicitly agrees. It also gives a negative rule ("The user's yes is not the code"), which is exactly the kind of when-not guidance that prevents misfires.

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