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

The description adds meaningful behavioral context beyond the annotations: the version is updated in place, animations whose bones the new rig still has are kept, the operation is paid and priced by size, and users should poll get_lowpoly_job. This is exactly the kind of consequence disclosure an agent needs and is consistent with the annotations.

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?

Three sentences deliver the core purpose, cost, mutation behavior, animation preservation, and next-step polling instruction. There is no filler, and the most important decision-relevant information is front-loaded.

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?

For an async, paid, mutating job, the description covers selection criteria, pricing model, in-place behavior, animation consequences, and the follow-up polling call. With a full input schema and an output schema present, no essential guidance is missing.

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%, with each parameter already clearly explained in the input schema. The description does not add parameter-specific detail beyond what the schema provides, so the baseline score of 3 is appropriate.

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 opens with 'Rebuild the skeleton of a rigged Low-Poly model version', using a specific verb and resource. Examples like 'move a pivot or add bones' clarify intent, and the mention of unrigged versions distinguishes it from related start_lowpoly_* tools.

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 clearly states when to use the tool (re-rigging a rigged version) and gives an exclusion: versions without a rig are rigged for free by their first animation instead. It does not explicitly name alternative sibling tools, so it stops just short of full alternative routing.

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