text-to-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 of behavioral disclosure. It mentions the tool's usefulness but doesn't describe critical behavioral traits such as whether it modifies the original body, creates a new body, requires specific permissions, has side effects, or what the output looks like. The description lacks details on mutation behavior, error conditions, or performance considerations.

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 concise and front-loaded, with two sentences that directly state the purpose and utility. There's no wasted text, and it efficiently communicates the core functionality. However, it could be slightly more structured by separating usage scenarios or adding bullet points for clarity.

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 complexity of a 3D modeling tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects, output format, error handling, or prerequisites. For a tool that likely performs geometric operations with potential side effects, more context is needed to guide an AI agent effectively.

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 already documents all parameters thoroughly. The description adds minimal value beyond the schema, as it doesn't explain parameter interactions, provide examples, or clarify semantics like how 'edge_indices' should be selected or what 'continuity' means in practice. 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 tool's purpose: 'Create a surface patch from boundary edges of an open body or selected edges.' It specifies the verb ('create'), resource ('surface patch'), and source ('boundary edges'). However, it doesn't explicitly differentiate from sibling tools like 'boundary_fill' or 'offset_surface', which might have overlapping functionality in surface modeling contexts.

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 implied usage guidance: 'Useful for closing open surfaces or creating complex surface shapes.' This suggests when to use the tool, but it doesn't explicitly state when not to use it or name alternatives among the many sibling tools. For example, it doesn't compare to 'boundary_fill' or 'thicken_surface' for similar operations.

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