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

Annotations cover the safety profile (readOnlyHint=false, idempotentHint=true, destructiveHint=false), and the description adds behavior annotations do not: the call is asynchronous, returns immediately with a running job, and the model-design step takes minutes. The polling cadence and expected job state are genuinely useful 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?

Front-loaded with the verb, the async nature, and the polling workflow; no sentence is padding. Since there is no output schema, the short Returns section earns its place, though the text is slightly loose in formatting.

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 correctly supplies the return contract (job_id, status 'running', and what get_status eventually yields). The remaining gap is the undocumented parameter set, which is mitigated but not eliminated by the reference to generate_dataset.

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 0% across 6 parameters, so the description must carry the meaning. It adds real context for `request` (plain-English, model designs the tables) but handles the other five only by deferring to generate_dataset's arguments, forcing a cross-tool schema lookup for ddl, seed, schema, blueprint and research.

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 ('Start a generation') plus the distinguishing scope ('in the background'), and names the immediate return value (job_id). This makes it clearly separable from the sibling generate_dataset, which is the synchronous counterpart.

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 concrete when-to-use guidance ('for any plain-English request') and a full follow-up workflow: poll get_status(job_id) every 20-30 seconds until done. It does not state when NOT to use it (e.g. when you want the dataset inline rather than a job), which keeps it short of a 5.

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