check_model

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

With no annotations provided, the description carries the full burden of transparency. It comprehensively lists the types of issues checked and states the return format ('grouped issue list'). No behavioral contradictions are present.

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 appropriately sized and front-loaded with the main purpose. It uses a structured list of issues. While slightly long, every sentence adds value. A minor improvement would be to further condense the parenthetical examples.

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's simplicity (no parameters, no output schema), the description is complete. It explains what the tool detects, when to run it, what to do with results, and the return format ('grouped issue list'). No additional information is required.

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 tool has no parameters, so the schema coverage is effectively 100%. The description adds significant value by explaining the tool's purpose and behavior, which is more than the baseline 3 for high coverage.

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 that the tool audits the model for specific problems such as untextured faces, zero-area UVs, degenerate cubes, unparented cubes, and Z-fighting. It distinctly differentiates from sibling tools, none of which perform a similar audit.

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 provides clear when-to-use guidance: 'Run this after building and before/after texturing, then fix what it reports.' It also gives specific advice for fixing coplanar_overlap. However, it does not explicitly state when not to use the tool or mention alternatives, which prevents a perfect score.

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