train_caption_dataset

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

No annotations provided, so description carries the burden. It discloses key behaviors: uses user's Claude subscription (billing), writes to .txt files, sequential processing, per-file failure handling. Could mention idempotency or overwrite behavior, but sufficient for understanding side effects.

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?

Two sentences effectively convey core action, workflow placement, error handling, and optional parameters. No redundant text; every word earns its place. Front-loaded with the main purpose.

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, but description doesn't explain return values (likely success summary). It does mention per-file failure reporting. Considering sibling tools (train_start, train_list_datasets), the overall context is complete, but a note on expected outcomes would improve completeness.

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% with clear param docs. The description adds value by explaining guide ('steers all captions') and trigger ('prepended to each') beyond schema. For name and only, schema already suffices, so description provides marginal extra context.

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 clearly states the tool's purpose: caption a staged dataset using Claude and write captions to .txt files. It specifies sequential processing and positions the tool in the workflow ('Use after gathering images, before train_start'). This distinguishes it from sibling tools like train_caption_image (single image) and train_prepare_dataset.

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?

Explicit guidance on when to use ('after gathering images, before train_start') and mentions error handling behavior. Does not explicitly state when to avoid or compare to alternatives like train_caption_image, but the context is clear enough for appropriate use.

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