generate_hyper3d_model_via_images

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the 3D asset has built-in materials, the model has normalized size (requiring potential re-scaling), and it returns a success/failure message. However, it omits details like processing time, error handling, Blender integration specifics, or any rate limits/permissions needed.

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 purpose statement, key behavioral notes, and a dedicated parameters section. Each sentence adds value, such as the normalization and re-scaling advice. It could be slightly more concise by integrating the parameters explanation more seamlessly, but overall it's efficient and front-loaded.

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 complexity (3D generation, Blender import), no annotations, and no output schema, the description is moderately complete. It covers parameters well and mentions critical behaviors like built-in materials and normalized size. However, it lacks details on the return message format, error conditions, or integration with sibling tools like 'import_generated_asset' or 'poll_rodin_job_status'.

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?

With 0% schema description coverage, the description compensates fully by explaining all three parameters. It clarifies that input_image_paths and input_image_urls are mutually exclusive based on Hyper3D Rodin mode, specifies they must be lists even for single images, and details bbox_condition as an optional list of 3 ints controlling [Length, Width, Height] ratio. This adds significant meaning beyond the bare schema.

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: 'Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.' It specifies the verb (generate), resource (3D asset), and destination (Blender). However, it doesn't explicitly differentiate from its sibling 'generate_hyper3d_model_via_text' beyond the input type, missing a direct comparison.

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 clear context on when to use this tool: for generating 3D assets from images via Hyper3D and importing to Blender. It mentions the dependency on Hyper3D Rodin's mode (MAIN_SITE vs. FAL_AI) to choose between input_image_paths and input_image_urls. However, it lacks explicit guidance on when to use this vs. 'generate_hyper3d_model_via_text' or other 3D asset tools like 'download_polyhaven_asset'.

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