mathematical_functions

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

With no annotations, the description must disclose behavioral traits (e.g., read-only, error behavior, performance). It only says it 'calculates' without mentioning idempotency, edge cases, or side effects. This is too minimal for safe invocation.

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: one sentence and two examples. It front-loads the purpose and gives practical usage patterns. Every part is useful and there is no fluff.

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 5 parameters, no output schema, and many siblings, the description is incomplete. It lacks return value explanation, error handling (e.g., invalid function, domain errors), and differentiation from similar tools like expression_evaluator or precision_calculator.

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 coverage is 100%, so baseline is 3. The description adds examples showing usage of base and angle_unit, but does not add meaning beyond the schema descriptions. It provides no extra context for functions, precision, or value ranges.

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 explicitly states it's a mathematical function calculation tool and lists supported categories (trigonometric, logarithmic, exponential). This clearly distinguishes it from siblings like basic_arithmetic (simple operations) or calculus_engine (derivatives/integrals).

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 no guidance on when to use this tool vs. alternatives. Given many specialized siblings (e.g., expression_evaluator, probability_calculator), the lack of 'when not to use' leaves the agent with insufficient context for appropriate selection.

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