interpret_natural_language

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

No annotations are provided, and the description does not disclose side effects, authentication needs, rate limits, or any behavioral traits beyond returning code and result. The example gives return format but lacks safety or constraint information.

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 concise with clear sections (Args, Returns, Example). Every sentence adds value, and the structure is easy to scan. The colloquial 'This is magic' is negligible.

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 (one parameter) and the availability of an output schema (implied by the return description), the description adequately covers the conversion process and return format, though it lacks explicit details about output schema fields.

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 only parameter 'text' has no schema description (0% coverage), but the description compensates by explaining it is a natural language description and providing examples. It adds clear meaning beyond the schema's title and type.

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 converts natural language mathematical descriptions to Wolfram Language code. It provides examples and distinguishes its unique natural language input from sibling tools like mathematica_integrate or mathematica_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 tells users to 'describe what you want in English' but does not guide when to use this tool versus more specific siblings (e.g., mathematica_integrate for integrals). No explicit when-not or alternative recommendations.

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