ceiling_multiple

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the rounding direction ('up'), the rule ('away from zero'), and the domain/category context. However, it does not cover edge cases like negative numbers or zero significance, which could be relevant.

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 highly concise and front-loaded, with two sentences that directly state the tool's function and behavior. Every sentence earns its place by providing essential information without redundancy or 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 the tool's low complexity (2 parameters, no output schema, no annotations), the description is reasonably complete but could be improved. It covers purpose and behavior but lacks details on parameter handling, edge cases, or example outputs, which would enhance usability for an AI agent.

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 0% description coverage, so the description must compensate. It explains the parameters' roles ('number' to round, 'significance' as the multiple) but does not provide details on constraints, formats, or examples. This adds basic meaning but leaves gaps in parameter understanding.

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 with a specific verb ('Round up') and resource ('a number'), and distinguishes it from siblings by specifying 'to the nearest multiple of significance' and 'Always rounds away from zero.' It differentiates from generic rounding tools like 'ceil' or 'round_number' by focusing on multiples.

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 clear context for when to use this tool (rounding up to a multiple), but does not explicitly state when not to use it or name alternatives. It implies usage for arithmetic rounding scenarios but lacks explicit exclusions or sibling comparisons.

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