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 of behavioral disclosure. It mentions the conversion action but fails to describe critical traits: whether this is a destructive operation (e.g., does it delete the original curve?), permission requirements, error handling, or side effects. This is inadequate for a mutation tool with zero annotation coverage.

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 brief sections for arguments and returns. It avoids unnecessary fluff, but the 'Args' and 'Returns' sections could be integrated more seamlessly into the main text for better flow.

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 tool's complexity (a mutation with one parameter) and the presence of an output schema (which covers return values), the description is minimally adequate. However, it lacks details on behavioral aspects like error cases or side effects, which are crucial for safe invocation without annotations.

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 compensate. It adds meaning by explaining that 'curve_name' is the name of the curve object to convert, which clarifies the parameter's role. However, it doesn't provide details on format constraints (e.g., case sensitivity, existence checks) or examples, leaving gaps in understanding.

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: converting a curve object to a mesh object. It specifies the verb ('convert') and resource ('curve object'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'create_curve' or 'create_object', 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?

No guidance is provided on when to use this tool versus alternatives. The description lacks context about prerequisites (e.g., needing an existing curve object) or comparisons with other tools that might create or modify meshes, such as 'create_object' or 'remesh'. This leaves the agent without usage direction.

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