generate_model

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

With no annotations provided, the description fully carries the behavioral disclosure burden. It reveals timing differences (~20s vs 3-4 min), the effect of auto_cutout on transparency, the meaning of finish=True, and the blocking queue behavior. This is exceptionally transparent for an AI agent.

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 tightly packed into a few sentences with no filler. It front-loads the core purpose, then concisely explains options and side effects. Every sentence adds value, and the structure is logical (purpose → options → behavior).

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 params, output schema exists), the description covers the main workflow flags, timing, queue behavior, and references finish_model. It does not mention prepare_concept as a prerequisite or explain output_path, but these are minor gaps. The presence of an output schema means return value details are not required, so the description is largely complete.

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 add meaning. It does for paint, auto_cutout, finish, and texture_size (including allowed values 512|1024|2048). It omits seed and output_path, but these are self-explanatory from their names and defaults. The description compensates for most key parameters, though not all.

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 opens with a specific verb+resource: 'Turn a concept image into a textured 3D model (GLB).' It clearly distinguishes this tool from siblings by focusing on generation, while finish_model is referenced as a separate follow-up step. The GLB format specification adds precision.

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 usage contexts: paint=False for shape-only, finish=True for game-look pass with a pointer to finish_model, and auto_cutout for background removal. It also mentions the single-job queue, which tells the agent when to expect blocking. These are clear when/when-not scenarios.

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