remove_mesh_floating_regions

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

No annotations provided, so the description carries full burden. It discloses that the tool identifies and removes small components, defaults to keep_largest=True, and notes that output defaults to overwriting input. It does not mention permissions or error handling, but covers key behavioral aspects.

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 compact paragraph with front-loaded purpose. Each sentence adds value: problem statement, tool action, and parameter list. No redundancy or filler.

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 output schema and 0% schema coverage, the description covers purpose, parameters, and return value (stats dict). It lacks specifics on error handling or file format constraints, but is complete for typical use.

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 0%, but the description adds meaningful explanations for each parameter: file_path (input), output_path (overwrite default), keep_largest (keep only largest), min_triangle_pct (threshold when keep_largest=False). This compensates fully for the schema gap.

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 ('Remove') and the resource ('small disconnected components/floating geometry'), and distinguishes it from sibling tools like split_mesh_by_component by focusing on removing small components. The context of downloadable models with support pillars provides specific use case.

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 explains the typical scenario (downloads and marketplace models) and the parameters guide usage (e.g., keep_largest default). However, it doesn't explicitly compare to sibling tools or state when not to use, but the context is sufficient for the targeted task.

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