blender_ai_generate

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

The description discloses that the tool uses external backends, requires API keys, and performs a multi-step process (poll, download, import). Since no annotations are provided, the description carries the burden. However, it lacks details on error handling or rate limits.

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 concise at two paragraphs and front-loads the main purpose. It could benefit from more structured formatting (e.g., bullet points for parameters), but it is not overly verbose.

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 (7 parameters, 3 backends, 2 operations) and no output schema shown, the description is incomplete. It fails to cover parameter details and return values, leaving gaps for effective agent usage.

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 input schema has 7 parameters with 0% description coverage. The description only mentions operations and backends but does not explain key parameters like prompt, image_path, object_name, output_format, or poll_timeout, leaving the agent uninformed about their meaning.

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 tool generates 3D meshes via external AI backends and imports into Blender, with specific operations (generate, list_backends) and backends (tripo, rodin, hunyuan). It is distinct from siblings like blender_mesh or blender_render, which handle different tasks.

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 specifies when to use the tool: for AI-generated 3D content, and mentions prerequisite API keys. It does not explicitly exclude other scenarios or compare to siblings, but the context is adequate for most users.

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