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 burden. It discloses the output: 'Returns GLB, FBX, OBJ, and USDZ download links with optional PBR textures.' However, it omits important behavioral traits such as whether the operation is asynchronous (despite the 'wait_for_completion' parameter), any side effects, or requirements like API keys. It provides some transparency but has notable gaps.

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 well-structured with a clear opening line, bullet points for best inputs and use cases. It is relatively concise, though the use case bullets are somewhat redundant with the best inputs. Each section serves a purpose, making the description easy to scan.

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 (7 parameters, no annotations, no output schema), the description is incomplete. It covers purpose and high-level usage but lacks details on parameter behavior, error handling, asynchronous behavior, and response format specifics. A more comprehensive description is needed for an agent to use this tool correctly without guessing.

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 only indirectly references 'image_url' (as the input image) and 'enable_pbr' (through 'optional PBR textures'). Parameters like 'ai_model', 'topology', 'target_polycount', 'should_remesh', and 'wait_for_completion' are not explained. The description adds minimal meaning beyond the schema's raw structure.

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's purpose: 'Turn any image into a 3D model using Meshy AI.' It specifies the action (turn image into 3D model) and the resource (image). It also distinguishes itself from siblings by being explicitly image-to-3D, while siblings like 'generate_3d_model' might accept other inputs. The use cases and best inputs further clarify the scope.

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 provides explicit guidance on when to use the tool through 'Best inputs' (e.g., product photos, clean backgrounds) and 'Use cases' (e.g., turning product photos into 3D assets). It does not explicitly state when NOT to use or suggest alternatives, but the context is sufficient for typical usage decisions.

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