dataset_status

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

With no annotations provided, the description carries the full responsibility for behavioral disclosure. It discloses that polls are incremental, that summary mode omits rows, and that completed jobs return signed download URLs. This covers key state changes and output behavior. It stops short of describing failure modes or rate limits, but the disclosed behaviors are accurate and non-misleading.

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 sentences, with the core purpose stated in the first phrase. Every clause adds critical information: incremental polling, the `since` mapping, summary mode, and completion behavior. There is no redundancy or filler, and the structure front-loads the most important context.

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 polling tool with 3 parameters and no output schema, the description covers all necessary usage aspects: how to poll incrementally, when to use summary mode, and what happens on completion. It does not describe the full response shape, but given the absence of an output schema, the mention of download URLs provides sufficient closure. An agent can confidently call this tool after create_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 100%, so baseline is 3. The description adds value by explaining the `since` parameter's role as a cursor from the previous poll's `nextCursor`, and how `mode` affects output (omitting rows). These insights go beyond the schema's terse 'Row cursor from previous poll' and 'summary: progress + steps only', making the parameters more semantically meaningful.

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 opens with a specific verb+resource: 'Poll a dataset job for progress, the collection trace (steps) and the rows so far.' This clearly distinguishes it from sibling status tools like search_bulk_status, crawl_status, or batch_status by explicitly naming the dataset job context. The addition of incremental polling semantics and summary mode further pinpoints its unique role.

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 description explains how to use the tool incrementally ('pass the previous response's nextCursor as since') and offers a lighter mode ('mode summary'). While it doesn't explicitly name alternatives or exclusion criteria, the context of dataset jobs and the polling pattern make the usage clear. It could benefit from an explicit note like 'use after create_dataset', but the information is present and actionable.

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