auto_caption_dataset

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

Beyond the annotations, the description discloses important behavioral traits: it uses GPU time, downloads the captioner model on first use, captions only uncaptioned files by default, uses different default prompts for image-only versus video/audio datasets, and can be tracked with get_job. These details materially help an agent understand cost, side effects, and follow-up actions.

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 compact and front-loaded: it starts with the action, then immediately clarifies default captioning scope, prompt behavior, resource cost, and job tracking. Every sentence adds useful information without repetition.

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?

The tool queues a GPU job and the description covers the most important operational context: cost, model download, default captioning behavior, prompt override, and tracking via get_job. An output schema exists, so return values need not be explained, but the missing parameter details for gpu_ids and caption_ext keep it from being fully complete.

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 0%, so the description must carry parameter meaning. It explains recaption and prompt well, including default prompt behavior, but does not describe dataset format, gpu_ids, or caption_ext at all, leaving three of five parameters undocumented.

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 states a specific verb and resource: queue ai-toolkit's built-in captioning job for a dataset, naming Qwen3-Omni and comparing it to the UI's Caption button. This clearly distinguishes the tool from sibling operations like set_captions and get_captions by framing it as the automatic captioning-job trigger.

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 implies when to use the tool by explaining that it behaves like the UI Caption button and that it captions only uncaptioned files unless recaption is set. However, it does not explicitly compare against alternatives such as set_captions or explain when not to use this tool.

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