is_perfect_number

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 states the tool checks for perfect numbers but does not describe any behavioral traits such as input validation (e.g., handling negative numbers or non-integers), performance characteristics, error handling, or output format. For a tool with no annotations, this is a significant gap in transparency.

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 extremely concise and front-loaded, with no wasted words. It directly states the tool's function and includes domain/category information efficiently. Every sentence earns its place, making it easy to grasp the purpose quickly.

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 (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavior, output, or edge cases. For a simple arithmetic check, this might suffice, but it does not provide a complete picture for reliable agent invocation without additional context.

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 1 parameter with 0% description coverage, but the description compensates by implicitly defining the parameter 'n' as the number to check. It adds semantic meaning by explaining what a perfect number is, which helps understand the parameter's role. However, it does not specify constraints (e.g., integer range) or examples, leaving some ambiguity.

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 ('Check') and resource ('a number'), and defines what constitutes a perfect number ('equals sum of its proper divisors'). It also distinguishes itself from siblings by specifying its domain and category ('Domain: arithmetic, Category: arithmetic_functions'), which helps differentiate it from other mathematical tools in the list.

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 implies usage in arithmetic contexts but does not explicitly state when to use this tool versus alternatives. While it mentions the domain and category, it lacks guidance on specific scenarios, prerequisites, or comparisons to similar tools (e.g., 'is_abundant_number' or 'is_deficient_number' from the sibling list). This leaves usage somewhat open to interpretation.

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