lcm

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

No annotations are present, so the description carries the full burden. It explicitly discloses the integer requirement, the return dictionary shape with either a result or an error, the ValueError condition, and the side effect of logging the operation and result. This is strong behavioral disclosure.

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 efficiently structured with Args, Returns, Raises, and Notes sections. Every sentence adds relevant operational information, with no filler or redundancy.

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?

For a single-parameter math tool with an output schema, the description covers input structure, constraints, return behavior, error handling, and logging. Nothing essential for an agent to invoke it correctly is missing.

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 coverage is 0%, but the description compensates by explaining that data is a TwoNumberOperation containing a and b and adding the critical integer constraint that the schema's 'number' type does not convey. It does not repeat parametric details already in the schema.

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 opens with a specific verb and target: 'Calculate the Least Common Multiple (LCM) of two numbers.' It then reinforces the meaning by stating the result is the smallest number divisible by both inputs, which clearly distinguishes it from the sibling gcd and other arithmetic tools.

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 clearly states the integer precondition and general operation, but it does not provide explicit when-to-use or when-not-to-use guidance relative to alternatives such as gcd. Usage is mostly implied by the mathematical operation named in the purpose.

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