scientific_calculation

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

Annotations already indicate read-only and idempotent behavior. The description adds useful behavioral context by explaining how secondary_value changes the meaning of value for trig functions and how it serves as exponent or log base.

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 and well-structured, using a clear Args format with no redundant or extraneous content. Every sentence contributes parameter semantics or operational detail.

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 description provides enough detail to invoke the tool correctly for common cases, including parameter roles and trig unit handling. It does not mention error cases or return details, but an output schema is present and the core behavior is sufficiently covered.

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?

Despite the schema lacking property descriptions, the description thoroughly explains all three parameters: operation lists the allowed values, value identifies the primary input and trig unit behavior, and secondary_value clarifies its role as exponent, log base, or degree-mode flag.

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 function as performing scientific mathematical calculations and enumerates the supported operations. It is distinct from sibling tools by covering scientific functions like power, sqrt, trig, and logs.

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 does not provide guidance on when to prefer this tool over siblings such as evaluate_math_expression or perform_arithmetic. It lists operations but lacks explicit usage conditions or selection criteria.

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