train_caption_image

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

Discloses it uses the user's Claude subscription (one vision turn) and returns caption without writing. No annotations provided, so description carries full burden. Missing potential error cases or prerequisites (e.g., path must exist under training root).

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 concise sentences, no fluff, front-loaded with core 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 describes return as 'bare caption'. Lacks details on error handling, return format, or cost implications beyond 'own Claude subscription'. Adequate but not comprehensive.

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%. Description adds meaning beyond schema: guide 'steers style', trigger is 'prepended by model'. Adds context for all parameters.

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?

Description clearly states it captions one dataset/training image using the user's own Claude subscription, distinguishing it from siblings like train_caption_dataset (writes directly) and train_dataset_update (saves reviewed captions).

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 tells when to use this tool (for a single image, review first) and when to use alternatives (train_caption_dataset to write directly, train_dataset_update to save). Also mentions optional guide text and trigger.

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