gcd_lcm

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

Annotations are absent, so the description carries full responsibility. It states the tool calculates GCD and LCM but omits key details: the format of the output (e.g., JSON object with 'gcd' and 'lcm' fields), handling of edge cases (negative numbers, zero), and any side effects. For a simple computation, this is minimally adequate.

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: two sentences plus a bullet list. No extraneous words. Every line conveys essential information efficiently.

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?

The tool is simple with two parameters and an output schema (unseen). The description covers the basic purpose but lacks details on output structure, error handling, or valid input ranges. It is adequate but not thorough for a tool with no annotations and 0% schema coverage.

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%, requiring the description to compensate. However, it merely paraphrases the schema ('a — First number', 'b — Second number') without adding constraints, ranges, or examples. The description adds little value beyond the parameter titles.

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 action 'Calculate GCD and LCM' with specific resource 'two numbers'. The verb and resource are precise, and it naturally distinguishes from sibling math tools that perform other calculations.

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 (e.g., fraction_calc, prime_operations). There is no mention of specific use cases or conditions for selection.

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