math_convert

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

No annotations are provided, so the description must carry the full burden. It discloses the core behavior (conversion and constant lookup) and lists supported unit categories, but does not discuss error handling, validation, or edge cases (e.g., invalid units).

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: it starts with the purpose, then lists parameters, provides examples, and ends with supported categories. It is slightly verbose but clear and efficient.

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 moderate complexity with 4 optional parameters and an output schema, the description adequately covers all aspects: purpose, parameter semantics, examples, and supported unit categories. No critical information 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?

The input schema has 0% description coverage, so the description fully compensates by explaining each parameter (value, unit_from, unit_to, constant) with examples. It clarifies that value is a numeric string and that constant is for querying constants.

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 performs unit conversion and physical constant queries. Examples and supported categories further clarify the purpose, and it is distinct from sibling tools like math_calculus or math_solve.

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 examples showing typical use cases but does not explicitly state when to avoid this tool or compare it to siblings. However, the sibling tools are in different domains (calculus, algebra, etc.), so the context is sufficient.

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