blender_vision_refine

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

The annotations are all false and provide little safety context, so the description carries the burden. It discloses what each operation outputs but does not mention side effects like file writes, executing arbitrary scripts, or scene modifications.

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 well-structured with a bullet list, return format, and an example. It is concise but could be improved by mapping parameters to each operation.

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?

With 9 parameters and three distinct operations, the description does not clarify which parameters apply to which operation, nor does it detail the output schema beyond a generic dict. This is insufficient for reliable tool selection and invocation.

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%, and only output_path and operation are illustrated. There is no explanation for goal, angles, script, resolution, include_multi_angle, or output_dir, leaving invocation ambiguous.

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 identifies the tool as an 'Agent vision refinement loop' with three specific operations: capture, review_bundle, and apply_scripts. This distinguishes it from sibling tools like blender_render or script_execute.

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 a vision-model-driven refinement workflow. It does not explicitly name alternatives or exclusions, but the operational steps make the intended use case evident.

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