text-to-model

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

No annotations are provided, so the description carries full burden. It mentions the tool lists faces with specific geometric data, but lacks behavioral details like whether it's read-only, if it requires specific permissions, how it handles errors (e.g., invalid body names), or if it has performance implications. This is a significant gap for a tool with no annotation coverage.

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 two sentences with zero waste. The first sentence front-loads the core functionality, and the second provides immediate usage context. Every word earns its place, making it highly efficient.

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 no annotations and no output schema, the description is incomplete. It explains what the tool does and why to use it, but lacks details on return format (e.g., structure of face data), error handling, or prerequisites. For a tool with 2 parameters and geometric output, more context is needed for full understanding.

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 the schema fully documents both parameters. The description does not add any parameter-specific information beyond what's in the schema (e.g., it doesn't clarify the relationship between body_name and component_name). Baseline 3 is appropriate when the schema does the heavy lifting.

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 action ('List all faces'), target resource ('of a body'), and specific attributes returned ('type, area, centroid, and normal'). It explicitly distinguishes from siblings by mentioning 'shell/sketch-on-face operations' as downstream uses, which helps differentiate from other list_* tools like list_bodies or list_edges.

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 context for when to use this tool ('to identify faces for shell/sketch-on-face operations'), giving practical guidance. However, it does not explicitly state when not to use it or name specific alternative tools for different scenarios, 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.