asset_train_brand_lora

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

The description discloses key behaviors: external HTTP call, packaging/validation, and output (lora_id). Annotations only provide openWorldHint, so the description adds value beyond structured fields.

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 three sentences, front-loading the purpose, then adding requirements and context. No wasted words, but could be slightly more concise.

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 training tool with 6 params and no output schema, the description covers inputs, process, output, and external dependencies, referencing more detailed docs. Lacks explicit return format but acceptable.

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 67%; the description adds meaning for 'name' and 'training_images' but not for 'rank' and 'steps', which are only given defaults. Some param info is provided but not comprehensive.

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 trains a brand-consistent LoRA from sample images and returns a lora_id, with a specific verb and resource, distinguishing it from siblings focused on asset generation/ingestion.

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 the tool requires a user-owned training endpoint and references documentation, but does not explicitly state when not to use or list alternatives among siblings.

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