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 of behavioral disclosure. It mentions that the tool returns a 'Confirmation dict with new origin location,' which adds some context about output. However, it fails to disclose critical behavioral traits such as whether this operation is destructive, requires specific permissions, or has side effects like affecting object transformations. For a mutation tool with zero annotation coverage, this is a significant gap.

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 appropriately sized, with a clear purpose statement followed by 'Args' and 'Returns' sections. Every sentence adds value, such as explaining parameter options and the return format. It could be slightly more concise by integrating the purpose into the parameter explanations, but overall it is efficient and front-loaded.

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 moderate complexity (2 parameters, one required), the description is fairly complete. It explains parameters thoroughly and notes the return format, and since an output schema exists, it doesn't need to detail return values further. However, it lacks context on usage scenarios or behavioral risks, which would enhance completeness for a mutation tool 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 description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains that 'name' is the 'Name of the object' and details the 'type' parameter with enum values and defaults (e.g., 'GEOMETRY', 'CURSOR'), including what each option does. This fully compensates for the lack of schema descriptions, making parameters clear and actionable.

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: 'Set the origin point of an object.' It specifies the verb ('Set') and resource ('origin point of an object'), making it understandable. However, it does not explicitly differentiate from sibling tools like 'set_location' or 'set_rotation', which also modify object properties, leaving room for improvement in sibling distinction.

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 lacks context about prerequisites (e.g., whether the object must be selected or in a specific mode) or exclusions. Without such information, users might struggle to apply it correctly in a workflow with many sibling tools.

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