blend-ai

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavioral traits. It mentions the action ('duplicate') and return value ('Confirmation dict'), but fails to cover critical aspects like permissions needed, whether the duplication is reversible, rate limits, or how it interacts with other tools (e.g., if it overwrites existing materials). This is insufficient 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 clear sections for args and returns. It uses minimal sentences without redundancy, though the 'Args' and 'Returns' labels are slightly verbose. Overall, it's efficient and easy to parse.

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 operation with 2 parameters), no annotations, and an output schema present, the description is moderately complete. It covers the basic action and parameters but lacks behavioral context and usage guidelines. The output schema likely details the 'Confirmation dict', reducing the need for return value explanation, but overall completeness is adequate with noticeable gaps.

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 for the lack of parameter documentation. It lists the parameters ('material_name' and 'new_name') and their purposes, adding meaning beyond the bare schema. However, it doesn't provide details like format constraints, examples, or validation rules, 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: 'Duplicate a material with a new name.' It specifies the verb ('duplicate') and resource ('material'), making the action unambiguous. However, it does not explicitly differentiate from sibling tools like 'create_material' or 'rename_object', which could handle similar operations, so it doesn't reach a score of 5.

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 such as prerequisites (e.g., whether the material must exist), comparisons to siblings like 'create_material' or 'duplicate_object', or any exclusions. This leaves the agent without usage direction beyond the basic action.

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