Model info

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

The description lists the returned summary items, but lacks details on behavior such as read-only nature, error handling for missing models, or any side effects. No annotations are provided, so the description carries full burden; it is adequate but not complete.

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 concise sentence that front-loads the key information. It is not verbose, but every word contributes. Could be slightly expanded without losing structure.

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 has one parameter and no output schema, the description provides the core return fields but omits details on preconditions (e.g., model existence) and error conditions. The context from sibling tools implies some usage context, but the description itself is incomplete.

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?

The only parameter 'model' is a string with no description in the schema (0% coverage). The tool description does not clarify the expected format (e.g., model name or ID). The description adds minimal semantic value beyond the schema.

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 it returns a summary of a model, including specific attributes like counts, materials, sections, loads, and bounding box. It effectively distinguishes from sibling tools like analyze or list_models by focusing on static model metadata.

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?

No explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., model must exist) or exclusions (e.g., not for analysis results). The agent must infer usage from context.

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