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 an 'Add' operation (implying creation/mutation) but doesn't clarify permissions needed, whether it's destructive to existing physics properties, error conditions, or rate limits. The return format is mentioned but without details on what 'Confirmation dict' contains or success/failure 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 with a clear purpose statement followed by parameter explanations and return information. Every sentence earns its place. It could be slightly more front-loaded by moving the return statement after the purpose, but 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 5 parameters with 0% schema coverage and no annotations, the description does a good job explaining parameters but lacks behavioral context. The presence of an output schema (true) means the description doesn't need to detail return values, but it should still cover more about the tool's operational behavior, prerequisites, and error handling for a physics simulation tool.

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 provides clear semantic explanations for all 5 parameters beyond just their names: object_name ('Name of the object'), type ('Rigid body type - ACTIVE or PASSIVE'), mass ('Mass in kg'), friction ('Surface friction coefficient 0.0-1.0'), restitution ('Bounciness 0.0-1.0'). This adds significant value over 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 tool's purpose: 'Add a rigid body physics simulation to an object.' This is a specific verb ('Add') + resource ('rigid body physics simulation') combination. However, it doesn't explicitly differentiate from sibling tools like 'add_fluid_sim' or 'add_cloth_sim', which are also physics simulations but for different types.

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. There are no mentions of prerequisites (e.g., object must exist), when-not-to-use scenarios, or comparisons to related tools like 'set_physics_property' or 'bake_physics'. Usage context is implied but not stated.

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