generate_model

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 the key actions (generate, save, return object_id) but omits details such as potential asynchronicity, failure modes, limits, or cost implications. The behavior is outlined but not fully transparent.

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 a single, efficient sentence that front-loads the purpose, method, inputs, and output. Every word adds value; there is no fluff or redundancy.

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 lack of output schema and annotations, the description is minimal. It covers the core flow (generate, save, return ID) but does not explain how the object_id is used, error handling, or prerequisites. For a tool with 4 parameters and no output schema, more detail would be beneficial.

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%, so each parameter has a description in the schema. The tool description does not add new semantic information beyond what the schema already provides, merely restating the input sources. 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?

The description clearly states the verb 'generate', the resource '3D model', the method 'Tripo 3D', the input sources ('text prompt or an image'), and the outcome ('save it to Polar Cloud, and return object_id'). This is specific and distinguishes it from siblings like 'upload_model', which handles existing models.

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 implies usage when a text prompt or image is available for model generation, but it does not explicitly contrast with alternatives like 'upload_model' (for pre-existing models) or provide when-not-to-use guidance. The context is helpful but not directive.

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