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 states the action ('Make an object follow a path') but lacks behavioral details like whether this is a destructive/mutating operation, permission requirements, side effects (e.g., animation timeline impact), or error conditions. The mention of 'Returns: Confirmation dict with constraint details' hints at output but doesn't clarify behavior beyond the basic action.

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 front-loaded with the core purpose in the first sentence, followed by structured Args and Returns sections. Every sentence earns its place: the purpose statement is direct, and the parameter explanations are necessary given the lack of schema descriptions. No wasted words.

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 2 parameters with 0% schema coverage and no annotations, the description does well on parameters and purpose but lacks behavioral context (e.g., mutation effects, prerequisites). The output schema exists (implied by 'Returns'), so explaining return values isn't needed, but overall completeness is adequate with clear gaps in usage and transparency.

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%, but the description compensates well by explaining both parameters in the Args section: 'object_name: Name of the object to animate along the path' and 'path_object: Name of the curve object to use as the path.' This adds clear meaning beyond the schema's bare titles ('Object Name', 'Path Object'), though it doesn't cover format details (e.g., naming conventions).

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 specific action ('Make an object follow a path'), the mechanism ('using a Follow Path constraint'), and the resources involved ('object' and 'curve object'). It distinguishes this from sibling tools like 'add_constraint' (generic) or 'create_curve' (creation only) by specifying the exact constraint type and purpose.

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. It doesn't mention prerequisites (e.g., existing objects/curves), exclusions, or related tools like 'add_constraint' for other constraint types. The agent must infer usage from the purpose alone.

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