blend-ai

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

With no annotations provided, the description carries the full burden. It clearly indicates this is a mutation operation ('Set'), describes what gets modified (material's Principled BSDF node color), and specifies the return format ('Confirmation dict'). However, it doesn't mention potential side effects, permission requirements, or error conditions that might be relevant for a mutation 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 perfectly structured with a clear purpose statement first, followed by well-organized parameter explanations, and ending with return information. Every sentence earns its place, with no redundant or unnecessary content. The formatting with clear sections (Args, Returns) enhances readability.

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 that this is a mutation tool with no annotations but with an output schema (implied by 'Has output schema: true'), the description provides good coverage. It explains the action, parameters thoroughly, and return format. The main gap is lack of behavioral context about potential errors or side effects, but the output schema reduces the need to fully describe return values.

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 schema description coverage is 0%, so the description must fully compensate. It provides complete semantic information for both parameters: 'material_name' is explained as 'Name of the material' and 'color' is detailed as 'RGBA color as a list of 4 floats (0.0-1.0)' with a concrete example. This adds substantial value beyond the bare schema.

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 specific action ('Set the base color'), target resource ('material's Principled BSDF node'), and scope ('base color'). It distinguishes this tool from other material-related siblings like 'set_material_blend_mode' or 'set_material_property' by focusing specifically on color setting for the Principled BSDF node.

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 implies usage context through the parameter explanations (e.g., material must exist, color format), but doesn't explicitly state when to use this tool versus alternatives like 'set_material_property' or 'create_principled_material'. No explicit exclusions or prerequisites are mentioned beyond what's implied by the parameters.

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