lcm

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 calculates LCM but does not describe any behavioral traits such as error handling (e.g., for non-integer inputs, negative numbers, or zero), performance characteristics, or output format. For a tool with no annotation coverage, this is a significant gap in transparency.

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—a single sentence that states the tool's purpose and includes domain/category information. It is front-loaded with the core functionality and avoids any unnecessary verbiage, making it 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 simplicity (2 parameters, no annotations, no output schema), the description is minimal. It covers the basic purpose but lacks details on behavior, output format, error handling, or usage context. For a tool with no structured support, the description should provide more completeness to guide the agent effectively.

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 specifies that the tool calculates LCM for 'two integers', which directly maps to the two parameters (a and b) in the input schema. Since schema description coverage is 0%, the description compensates by clarifying the parameter types and purpose, adding meaningful context beyond the bare schema. However, it does not detail constraints or examples (e.g., range, handling of negatives).

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 explicitly states the verb ('Calculate') and resource ('Least Common Multiple (LCM) of two integers'), making the purpose specific and clear. It distinguishes this tool from sibling tools by focusing on LCM calculation, which is a distinct mathematical operation among the many arithmetic/number theory tools listed.

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 mentions the domain and category, but does not specify scenarios, prerequisites, or comparisons to other tools (e.g., gcd or other arithmetic operations). This lack of usage context leaves the agent without direction on appropriate application.

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