blender_ai_set_backend

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

The description makes it clear that this is a state-changing preference operation scoped to model generation, which is the core behavior. However, with no annotations, it doesn't say whether the preference persists, whether the backend must already be available/configured, or whether validation/errors occur for unknown backend names.

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?

A single, front-loaded sentence with no filler. Every clause earns its place: the verb, the scoped resource, the purpose, and useful examples.

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 simple two-parameter setter with a clear schema, this is minimally viable. It lacks context about discoverability/validation of backend names and does not reference the related backend-management siblings, so an agent navigating the large toolset gets only partial guidance.

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 coverage is 100%, so the baseline is 3. The description adds value by giving concrete example values for the backend parameter ('comfyui', 'rodin', 'triposr') beyond the generic schema description, though it doesn't elaborate on how prefer_local interacts with the chosen backend.

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 uses a specific verb ('set') and resource ('preferred AI backend') and scopes it to model generation, with concrete backend examples. It is clear on its own, though it doesn't explicitly distinguish itself from the sibling blender_ai_configure_backend, so an agent still has to infer the difference from the word 'preferred'.

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?

No guidance is given about when to call this tool versus closely related siblings such as blender_ai_list_backends, blender_ai_probe_backends, or blender_ai_configure_backend. There are no exclusions or alternative conditions, so an agent must guess the right selection context.

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