blend-ai

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

No annotations are provided, so the description carries full burden. It mentions the tool 'adds' a simulation, implying mutation, but lacks details on permissions, side effects (e.g., whether it overrides existing simulations), performance impact, or error handling. The return value is briefly noted but without behavioral context like confirmation format or failure modes.

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 and front-loaded with the core purpose, followed by Args and Returns sections. It avoids redundancy, but the 'Returns' line could be more concise (e.g., 'Confirmation dict' alone). Overall, it's efficient with minimal waste.

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 no annotations, 0% schema coverage, and an output schema present, the description is moderately complete. It covers parameters and return type but lacks behavioral details (e.g., mutation effects, error cases). For a physics simulation tool with siblings, more context on usage and limitations would improve completeness.

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 lists all three parameters with brief semantics: object_name identifies the target, quality defines simulation steps (1-80), and mass specifies cloth weight in kg. This adds meaningful context beyond schema titles, though it doesn't explain defaults or interactions between parameters.

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's purpose: 'Add a cloth physics simulation to an object.' It specifies the verb ('add') and resource ('cloth physics simulation'), distinguishing it from siblings like add_fluid_sim or add_rigid_body. However, it doesn't explicitly differentiate from other physics-related tools beyond naming, which prevents a perfect score.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., requiring a mesh object), exclusions (e.g., not suitable for non-mesh objects), or comparisons to siblings like add_rigid_body or bake_physics. Usage is implied but not explicitly stated.

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