add_texture

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

With no annotations, the description carries full burden but only partially discloses behavior: it states outputs (maps and formats) but omits side effects, limitations, or authentication needs. It does not contradict any annotations.

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 bullet points and use cases, front-loading the main action. While it is not extremely terse, every sentence adds value and supports understanding.

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 9 parameters, no annotations, and no output schema, the description is incomplete. It lacks parameter explanations, return structure, and error handling, making it insufficient for an AI agent to invoke correctly without additional context.

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%. The description only explains the model_url parameter implicitly, leaving other parameters (object_prompt, style_prompt, etc.) unexplained. This fails to compensate for the lack of schema-level descriptions.

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 that the tool applies AI-generated textures to an existing 3D model, specifying the input format (GLB/OBJ) and generated maps. It also lists use cases that differentiate it from sibling tools like generate_3d_model.

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 lists use cases but does not explicitly guide when to use this tool versus alternatives. It implies context via examples but lacks explicit when-to-use or when-not-to-use criteria.

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