remainder

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 mentions 'IEEE remainder operation,' which implies adherence to a specific floating-point standard, but doesn't describe key behaviors: error handling (e.g., division by zero), precision limitations, return format, or edge cases (e.g., NaN/infinity inputs). For a mathematical tool with zero annotation coverage, this is a significant gap.

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 a single, efficient sentence: 'Calculate floating-point remainder of division (IEEE remainder operation). (Domain: arithmetic, Category: core).' It's front-loaded with the core purpose and avoids redundancy. The parenthetical domain/category could be omitted but doesn't detract significantly.

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 mathematical nature, no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't explain the return value (e.g., remainder as a number), error conditions, or how it differs from similar sibling tools. For a core arithmetic operation, more context on behavior and usage is needed.

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 2 parameters (x, y) with 0% description coverage, so the schema provides no semantic information. The description doesn't add any parameter details—it doesn't explain what x and y represent (e.g., dividend and divisor) or their constraints. However, for a simple arithmetic operation with only two numeric inputs, the baseline is 3 as the purpose is clear enough to infer parameter roles.

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: 'Calculate floating-point remainder of division (IEEE remainder operation).' It specifies the verb ('calculate'), resource ('floating-point remainder'), and operation type ('IEEE remainder operation'), which distinguishes it from basic modulo operations. However, it doesn't explicitly differentiate from sibling tools like 'modulo' or 'fmod', which might have similar mathematical purposes.

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 versus alternatives. While it mentions 'IEEE remainder operation' (which hints at a specific mathematical standard), it doesn't explain when this is preferable over other remainder/modulo tools in the sibling list (e.g., 'modulo', 'fmod'). There are no explicit when/when-not statements or named alternatives.

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