rig_model

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

No annotations provided so description bears full responsibility. It discloses the core behavior (auto-rigging) and input/output flow but does not mention edge cases (e.g., conflicting inputs), error handling, or side effects. Adequate but not exhaustive.

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, no redundancy. Front-loads the purpose, then efficiently details inputs and output linkage. Every sentence is essential.

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?

For a tool with 4 parameters, no required fields, and no output schema, the description captures the primary workflow and connects to a sibling tool. However, the lack of explanation for height_meters and timeout reduces completeness slightly.

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 description must compensate. It explains input_task_id and model_url as identification methods, adding meaning to these parameters. However, height_meters and timeout are not mentioned, leaving gaps in parameter understanding.

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 auto-rigs a humanoid model for animation, specifies inputs (input_task_id or model_url), and output (rig_task_id), distinguishing it from sibling tools like animate_model by directly linking the output.

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?

Provides clear context on how to identify the model (via prior task ID or public URL) and directs the agent to feed the result to animate_model. Lacks explicit when-not-to-use or alternatives but is sufficient given sibling differentiation.

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