unit_conversion_radiansToDegrees

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

With no annotations provided, the description carries the full burden. It effectively discloses key behavioral traits: it describes the return format (JSON string with value and units), mentions dependencies (Turf.js and Node.js environment), and outlines error conditions (JavaScript execution failures, timeouts, or input format errors). This provides good transparency for a tool with no annotations.

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 with clear sections (Args, Returns, Raises, Example, Notes), but it is somewhat verbose. Some information (like the full example and detailed notes) could be more concise. However, all content is relevant and adds value, avoiding 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?

Given the tool's simplicity (1 parameter, no output schema, no annotations), the description is highly complete. It covers purpose, parameter details, return format, error handling, examples, and implementation notes. This provides all necessary context for an AI agent to understand and use the tool 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 input schema has 0% description coverage (only type information), but the description fully compensates by providing detailed parameter documentation: it defines 'radians' as a float value to be converted, gives an example (3.14159), and explains its role in the conversion. This adds significant meaning beyond the bare 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 clearly states the tool's purpose: '将弧度转换为角度' (convert radians to degrees). It specifies the exact transformation (radians to degrees) and distinguishes itself from sibling tools like 'degreesToRadians' and other unit conversion tools by focusing on this specific direction of conversion.

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 clear context for when to use this tool: for mathematical calculations and trigonometry involving radian-to-degree conversion. It mentions the conversion formula and typical use cases, but does not explicitly state when NOT to use it or name specific alternatives among siblings (e.g., 'degreesToRadians' for the reverse operation).

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