blender_ai_generate

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

Annotations are all false, so the description carries the transparency burden. It discloses that external AI backends are used, that API keys are required, and that the generate operation involves polling and import. It also details the return format and gives an example. This goes beyond basic mutation info, though it doesn't mention potential network delays, costs, or 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?

The description is well-structured with sections for backends, operations, return format, and examples. It front-loads the main purpose, then provides necessary operational details without unnecessary fluff. Every section 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?

Given the tool's complexity (multiple backends, operations, API keys), the description covers key aspects: backends, env vars, operations, return format, and a usage example. It doesn't detail every parameter or error handling, but with the input schema present and an output schema, it's reasonably complete for an AI agent to use effectively.

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%, so the description must explain parameters. It only shows an example using operation and prompt, and the operations list implies prompt/image_path. However, backend, object_name, poll_timeout, and output_format are not described. The agent must infer their meanings from names and defaults, which is insufficient for a 7-parameter tool.

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 a specific and clear statement: 'Generate 3D meshes via external AI backends and import into Blender.' It also enumerates distinct operations (generate, list_backends), which further clarifies its scope. This clearly distinguishes it from sibling tools like blender_mesh or blender_import that handle mesh operations or imports without AI generation.

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 communicates when to use the tool: when you need AI-based 3D generation from text/image with backend options, and it explains backend configuration via environment keys. It doesn't explicitly exclude alternatives or name sibling tools, but the context is clear enough that an agent would know to use this for AI generation versus manual modeling/import.

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