blender_ai_pipeline_generate

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

With no annotations provided, the description carries the behavioral burden. It does disclose the pipeline stages, rough runtime for quick ('15s blob'), and that it returns pipeline status with results from each stage. However, it does not say whether the call blocks or returns an async status to poll, whether it modifies the current Blender scene, or what side effects a full import+cleanup+texture run may have; these are meaningful gaps for a complex pipeline tool.

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 two sentences, front-loaded with the pipeline scope, and every phrase carries information about stages or quality tiers. It is dense but not bloated, and the length is justified by the tool's complexity.

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?

The description gives a solid high-level overview and parameter-tier guidance, and the schema covers individual parameter meanings. But for an 11-parameter pipeline with no output schema or annotations, it omits the invocation contract: async vs synchronous behavior, how to interpret or poll the returned status, and whether the pipeline will modify the current Blender scene. The overall picture is adequate but not fully complete.

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 11 parameters. The description adds some color ('15s blob', 'good single-image', 'best structure', 'part separation') but essentially restates the pipeline_preset enum meanings. It provides no additional syntax, defaults, or cross-parameter guidance beyond what the schema offers.

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 end-to-end workflow: reference photo/prompt -> 3D model generation -> import -> cleanup -> UV -> texture -> optional MSFS prep. This clearly identifies the tool's resource and scope, and distinguishes it from the many single-step blender_ai_* siblings such as blender_ai_generate_model or blender_ai_mesh_cleanup.

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 quality-tier list gives useful internal guidance on choosing quick vs standard vs multiview_quality vs vehicle_components, and the 'full AI pipeline' phrasing implies when to prefer this tool over granular siblings. However, it never explicitly states when to use this tool instead of alternatives like blender_ai_generate_model, blender_ai_mesh_cleanup, or blender_ai_auto_uv; usage is implied rather than stated.

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