divide

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 only states the basic operation ('Divide the first number by the second number') without mentioning critical behaviors such as error handling (e.g., division by zero), input constraints (e.g., numeric types), or output format (e.g., floating-point result). 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 extremely concise and front-loaded, consisting of a single, direct sentence that states the core functionality, followed by brief domain/category tags. There is no wasted verbiage or redundancy, making it efficient and easy to parse for an AI agent.

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 simplicity (2 parameters, no output schema, no annotations), the description is incomplete. It fails to address key contextual elements like error conditions (division by zero), numerical precision, or return value details. While the operation is straightforward, the lack of behavioral and output information leaves significant gaps for reliable agent use.

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 input schema has 0% description coverage, but the description adds minimal semantic context by specifying 'first number' and 'second number' for parameters 'a' and 'b'. This clarifies the order of operands, which is essential for division. However, it doesn't detail data types, ranges, or special cases (e.g., 'b' cannot be zero), so it only partially compensates for the schema's lack of descriptions.

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 with a specific verb ('Divide') and identifies the resources ('first number by the second number'), making the operation unambiguous. It also includes domain/category context ('Domain: arithmetic, Category: core'), which helps situate it. However, it doesn't explicitly differentiate from sibling tools like 'element_wise_divide' or 'quotient', which perform similar division operations, so it falls short of 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?

The description provides no guidance on when to use this tool versus alternatives. It lacks any mention of prerequisites, constraints (e.g., division by zero), or comparisons to sibling tools like 'element_wise_divide' or 'quotient', leaving the agent without explicit usage context. The domain/category hint is minimal and doesn't substitute for practical guidelines.

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