rig_model

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

With no annotations, the description carries the burden. It discloses inputs and that it returns a rig_task_id, implying an asynchronous task, but it does not say that the returned ID must be polled/used with a status tool or what side effects occur beyond creating the rig task.

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?

Three short sentences, front-loaded with the action, then source identification, then return value. Every sentence earns its place; no filler or repeated schema metadata.

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 core call is understandable: pass a prior task ID or public URL and receive a rig_task_id. However, there are no annotations or output schema, and the description omits whether at least one of the two identifiers is required, what height_meters does, and how to follow up on the returned task.

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 clarifies the meaning and relationship of input_task_id and model_url, but it leaves timeout and height_meters completely unexplained, so an agent knows two of four parameters well.

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 verb and resource ('Auto-rig a humanoid model for animation') and distinguishes the outcome from generation or animation siblings by specifying the returned rig_task_id. It is immediately clear this is the rigging stage in the pipeline.

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 gives explicit input alternatives ('input_task_id (a prior Meshy generation) or a public model_url'), which tells the agent how to identify the model. It does not explicitly exclude cases or direct to a sibling like animate_model, but the context is clear enough.

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