blender_ai_list_backends

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

The word 'List' implies a read-only operation, and the description discloses the returned status categories. However, with no annotations provided, the description carries the full burden and does not clarify potential side effects, whether external services are queried, or whether the list reflects cached or fresh data.

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 single concise sentence, front-loaded with the action verb, and uses the parenthetical to compactly convey the returned status categories. There is no filler or redundant 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?

Given the simple tool shape—one optional parameter and no output schema—the description provides adequate return semantics by naming the status categories and listing backends. It could be more complete by clarifying the effect of available_only=false or by referencing related sibling tools, but the core information needed to call the tool is present.

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?

The single boolean parameter is fully described in the input schema, including its default value, so the description adds no additional parameter semantics. The schema already carries the load here, making the baseline score of 3 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 clearly states the verb 'List' and the resource 'AI model generation backends', and indicates the returned status categories. However, it does not explicitly distinguish itself from related siblings like blender_ai_probe_backends or blender_ai_model_status.

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 on when to use this tool versus related backend tools such as blender_ai_probe_backends, blender_ai_set_backend, or blender_ai_configure_backend. The agent must infer usage from the name and schema alone, and no exclusions or alternatives are mentioned.

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