generate_hyper3d_model_via_text

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

With no annotations, the description carries full responsibility. It discloses built-in materials, normalized size, and that it returns a success/failure message. It does not mention potential async behavior or side effects on the scene, but the core behavior is transparent enough.

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 concise and well-structured: two introductory sentences, a short parameter list, and a clear return statement. Every sentence adds value, and the parameter notes are directly useful.

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 description covers the main purpose, key parameters, and return type, but misses user_prompt semantics and any async or failure details. Given the complexity of a generation tool, it is adequate but not 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 coverage is 0%, so the description must compensate. It explains text_prompt must be in English and bbox_condition is an optional list of 3 floats controlling L/W/H ratio. However, it omits user_prompt entirely, leaving that parameter undocumented in both schema and description.

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 it generates a 3D asset using Hyper3D from a text description and imports it into Blender. The 'via text' phrasing distinguishes it from the image-based sibling tool, so it scores high on purpose clarity.

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?

It provides clear usage context: use a text description, the asset has built-in materials, and re-scaling may be needed after normalization. However, it does not explicitly contrast with alternative tools like image-based generation or mention when to prefer one over another.

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