blender_ai_evaluate

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

With no annotations provided, the description must carry the behavioral burden. It does disclose the external Ollama vision dependency and the return of structured scores and suggestions, but it omits operational details such as requiring a running Ollama server, network/performance implications, or likely failure modes.

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?

Two tight sentences with no filler. The core action, scope, method, and return type are all front-loaded, and every phrase contributes useful information.

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 a tool with no output schema and no annotations, the description should describe the return shape in more detail, the optional reference_image comparison behavior, and external service requirements. It covers the basics but leaves meaningful gaps for an agent deciding whether and how to call it.

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 six parameters. The description adds only the high-level notion of category-specific criteria, which maps to the category enum, but it does not add meaning beyond what the schema already provides.

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?

Description names a specific verb ('Evaluate'), a concrete resource ('any render or output'), and the evaluation method ('Ollama vision with category-specific criteria'). This clearly separates it from generation/refinement/validation tools in the sibling list.

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 implies usage in the context of evaluating outputs, especially after rendering or generation, but it does not explicitly state when to prefer this tool over alternatives like blender_ai_refine, blender_analyze_viewport, or blender_validate_mesh_quality. No exclusions or alternative routing is provided.

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