blend-ai

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

No annotations are provided, so the description carries the full burden. It mentions the tool adds a simulation but does not disclose behavioral traits like whether it modifies the object in place, requires specific permissions, has side effects (e.g., performance impact), or if it's reversible. This leaves significant gaps for an agent to understand the tool's behavior.

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 clear sections for Args and Returns. Each sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.

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 the complexity of adding a physics simulation, no annotations, and an output schema (implied by 'Returns'), the description is moderately complete. It covers parameters well but lacks behavioral context and usage guidelines. The output schema helps, but the description could better explain the confirmation dict or error cases.

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 effectively explains the semantics of all three parameters: 'object_name' (name of the object), 'type' (fluid type with specific values), and 'domain_type' (domain simulation type with values, conditional on type). This adds meaningful context beyond the bare schema, though it could detail format constraints or 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 ('Add a fluid physics simulation') and target ('to an object'), which is specific and actionable. However, it does not explicitly differentiate from sibling tools like 'add_particle_system' or 'add_rigid_body', which are also physics-related additions, leaving some ambiguity about when to choose this over alternatives.

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 other physics or simulation tools in the sibling list. It lacks context about prerequisites, alternatives, or exclusions, such as whether the object must be mesh-based or if other simulations are incompatible.

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