generate_3d_from_image

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

With no annotations, the description carries the full transparency burden. It does disclose the core behavior (image in, .glb out) and the return shape, which is useful, but it omits whether the operation is asynchronous, whether it blocks until the Meshy task completes, and what happens on failure.

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?

A single compact sentence front-loads the main purpose and then conveys the return value. There is no filler or repetition of schema titles.

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 tool has six parameters, no output schema, and no annotations, but the description covers only the input and coarse output. It lacks guidance on the optional parameters, polling/task behavior, and how to chain it with siblings such as get_meshy_task_status or render_turntable.

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 adds meaning only for the image parameter (public URL or local file path); timeout, enable_pbr, should_remesh, should_texture, and texture_prompt are left unexplained by 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 names a specific transformation ('Image-to-3D'), the accepted input type (photo/render via public URL or local file path), the output format (textured .glb), and the return payload (local path plus Meshy task id). This is specific enough to distinguish it from siblings like generate_3d_model or 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?

It clearly indicates when this tool applies: when the user has a single photo/render as input rather than a text prompt or multiple images. It does not explicitly name sibling alternatives or exclusion conditions, but the input-type context is unambiguous.

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