generate_3d_from_images

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

No annotations exist, so description carries full burden. It discloses input constraints (1-4 images) and output format, but does not mention behavioral traits like processing time, error conditions, or limitations on subject consistency. Adequate but not comprehensive.

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 very concise (one sentence) and front-loaded with key info, but it omits necessary parameter details, making it under-specified for effective use.

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?

Given the tool's complexity (multi-image 3D generation), zero schema coverage, and no output schema, the description is incomplete. It fails to explain parameters or process details, leaving significant gaps.

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%, and the description provides no explanations for any of the 6 parameters (e.g., texture_prompt, enable_pbr). The agent cannot infer parameter semantics from the description alone.

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 that it converts 1-4 reference images into a textured .glb model, specifying the input constraints (one subject, multiple images) and output format. It distinguishes from sibling 'generate_3d_from_image' by emphasizing multi-image input and higher fidelity.

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 use when multiple images are available for higher quality, implicitly contrasting with single-image tool. However, it lacks explicit 'when not to use' guidance or alternatives for other scenarios.

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