blend-ai

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a mutation operation ('Set'), implying it modifies the material. However, it doesn't disclose important behavioral traits: whether this requires specific permissions, if changes are reversible, what happens to existing material properties, or any side effects. The return value is mentioned but not explained beyond 'Confirmation dict.'

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 efficiently structured with clear sections: a purpose statement, Args section with parameter explanations, and Returns section. Every sentence earns its place, with no redundant information. The formatting with bullet-like sections makes it easy to parse while remaining compact.

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 this is a mutation tool with no annotations, 2 parameters, and an output schema exists, the description is minimally adequate. It covers what the tool does and parameter meanings, but lacks behavioral context about the mutation's effects, permissions, or error conditions. The existence of an output schema means return values don't need explanation, but other contextual gaps remain for a tool that modifies materials.

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 compensate. It successfully adds crucial semantics: it explains that 'material_name' identifies the target material and 'mode' is the blend mode with specific allowed values (OPAQUE, CLIP, HASHED, BLEND). This provides meaningful context beyond the bare schema, though it doesn't explain what each mode does or provide examples.

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 action ('Set the blend mode') and target resource ('of a material'), with the parenthetical '(EEVEE)' providing engine-specific context. It distinguishes from sibling tools like 'set_material_color' or 'set_material_property' by focusing specifically on blend mode. However, it doesn't explicitly contrast with other material-related tools beyond the parameter specificity.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., material must exist), when blend mode changes are appropriate, or what effects different modes have. While sibling tools include other material operations, there's no explicit comparison or decision criteria offered.

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