arithmetic

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It transparently states that the tool 'produces a new measurement,' indicating a non-destructive operation. However, it omits details about edge cases (e.g., division by zero), whether the result is saved or returned, or any permission/state requirements. This is an acceptable baseline but leaves room for more behavioral context.

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 exactly two sentences, directly states the core purpose and result, and uses efficient, specific language. There is no redundant or tangential content, and the most important information is front-loaded. This is exemplary conciseness.

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 (6 parameters, no output schema, no annotations), the description sufficiently covers the purpose, operation semantics, and the fact that a new measurement is produced. It could be more complete by mentioning how the result is returned or any prerequisites, but the essential behavior and context are adequately conveyed for an agent to understand and invoke the tool correctly.

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 provides full descriptions for all six parameters, so the baseline is 3. The description adds minimal extra value by clarifying the A/B ordering and giving examples of valid operation strings, but it does not compensate for any missing parameter details since the schema already covers them thoroughly.

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 specifies the tool's function as arithmetic operations on a pair of measurements, names the roles of the inputs (first = A, second = B), and provides concrete examples of operations and their use cases (division for transfer functions, subtraction for difference curves, inversion for correction curves). This distinguishes it from sibling tools like compare_measurements or average_measurements, which focus on other analysis tasks.

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 gives clear contextual guidance by mapping each operation type to its intended purpose, implying when the tool should be used. However, it does not explicitly contrast with alternatives or state when not to use it, such as when averaging or comparing measurements would be more appropriate. This is a minor gap, but the practical use cases are well illustrated.

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