text_to_model

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

Annotations already declare this is non-readonly, destructive, non-idempotent, and open-world. The description adds valuable behavior beyond that: it spends credits and returns immediately rather than blocking. It could note the credit magnitude or that repeated calls create new tasks, but the async/credit disclosure is solid.

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?

Two sentences, both front-loaded: the first states the purpose, the second the async pattern and follow-up call. No filler.

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?

An output schema exists and annotations cover the safety profile, so the description needn't explain returns. Given the tool's complexity (20 params, credit spend, async), the description adequately covers the essential non-structured facts (credits, task_id, wait_for_task follow-up), though it omits any hint of the parameter surface.

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 100% across all 20 params, including credit costs, model versions, and constraints, so the schema carries the full parameter burden. The description adds no parameter detail beyond prompting implicitly, so the baseline 3 is appropriate.

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?

States a specific verb and resource ('Generate a 3D mesh from a text prompt'), which cleanly separates it from image_to_model and multiview_to_model siblings. An agent can identify the tool's function from the first sentence alone.

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?

Explicitly describes the async workflow: it creates a Tripo task, returns a task_id immediately, and directs the agent to call wait_for_task next. It does not, however, say when to choose this over image_to_model or multiview_to_model for a given input modality.

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