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 states the tool sets scale and returns a dict, but doesn't cover important aspects like whether this is a destructive operation, permission requirements, error conditions (e.g., invalid object names), or side effects. For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps.

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 appropriately sized and front-loaded with the core purpose in the first sentence. The Args and Returns sections are structured clearly without unnecessary elaboration. Every sentence earns its place, though the formatting as a docstring could be slightly more concise for an MCP context.

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 has an output schema (which covers return values), the description doesn't need to explain returns in detail. However, as a mutation tool with no annotations and 2 parameters, the description should do more to cover behavioral aspects like side effects or error handling. It's minimally adequate but has clear gaps in context for safe usage.

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 meaningful context for both parameters: 'name' is clarified as 'Name of the object' and 'scale' as 'XYZ scale as a 3-element list/tuple'. With 0% schema description coverage, this compensates well by explaining what each parameter represents. However, it doesn't specify units, valid ranges, or format examples for the scale list.

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 verb ('Set') and resource ('scale of an object'), making the purpose specific and understandable. It distinguishes from siblings like 'set_location' or 'set_rotation' by focusing on scale, though it doesn't explicitly contrast with them. The description avoids tautology by explaining what the tool does rather than just restating the name.

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 doesn't mention prerequisites (e.g., whether the object must exist), exclusions, or related tools for different operations. While the context implies it's for scaling objects, there's no explicit usage context or comparison to sibling tools like 'set_location' or 'set_rotation'.

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