generate_3d_model

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

With no annotations provided, the description carries the full behavioral burden. It discloses that the tool blocks until ready, runs through preview and refine stages, applies textures, and returns the local .glb path and Meshy task IDs. This is solid behavioral context, though it omits failure modes or 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?

The description is three tightly written sentences with no filler. Core purpose is front-loaded, and pipeline details, parameter clarifications, and return information each earn their place.

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 6-parameter tool with no annotations and no output schema, the description covers key return values and blocking behavior, but leaves several parameters unexplained and provides no usage constraints or examples. It is adequate but has clear information 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%, so the description must compensate. It explains enable_pbr and texture_prompt, and implicitly covers prompt, but leaves timeout, art_style, and should_remesh undocumented. The partial coverage is helpful but not sufficient for all six 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?

The description clearly states a specific verb and resource: 'Generate a 3D model from a text prompt via Meshy.ai'. The preview→refine pipeline adds process detail, and the text-prompt input immediately distinguishes it from image-based siblings like generate_3d_from_image and generate_3d_from_images.

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 clearly implies when to use this tool: when you have a text prompt and want a 3D model. It does not explicitly name alternatives or exclusions, but the 'from a text prompt' framing provides enough contextual guidance given sibling names.

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