equal

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

No annotations are provided, so the description carries the full burden. It discloses the tool's behavior of checking exact equality and hints at precision issues with floating-point numbers, but does not cover other behavioral aspects like error handling, performance, or return format. It adds some context but lacks comprehensive behavioral details.

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 purpose and provide usage guidance. Every sentence adds value, and there is no redundant or unnecessary information, making it efficient and well-structured.

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. It covers purpose, usage guidelines, and parameter semantics adequately. However, it could be more complete by specifying the return value or handling edge cases, though the simplicity of the tool mitigates this gap.

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, but the description compensates by explaining that parameters 'a' and 'b' are 'two numbers' to be compared for exact equality. This adds meaningful semantics beyond the schema's type definitions, though it doesn't specify number types (e.g., integer vs. float) or constraints.

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 ('two numbers'), and explicitly distinguishes it from its sibling tool 'approximately_equal' by noting the exact equality requirement and recommending the alternative for floating-point numbers. This provides immediate clarity and differentiation.

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 explicitly provides usage guidance by stating when to use this tool ('Check if two numbers are exactly equal') and when to use an alternative ('For floating-point numbers, consider using approximately_equal instead'). This directly addresses the tool's context relative to its sibling, offering clear decision criteria.

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