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

Despite no annotations, the description clearly states the tool's behavior: it returns percentage, decimal form, and simplified fraction, with an example ('3 out of 4 yields 75%, 0.75, and '3/4''). It discloses the non-destructive, purely computational nature.

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 two sentences long, with the core purpose upfront. Every sentence adds information—no fluff or repetition.

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?

The description explains what the tool returns (percentage, decimal, fraction) and provides common use cases. With an output schema present (as indicated by context signals), the description is sufficient for an agent to understand inputs and outputs without additional details.

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?

Schema coverage is 100% with clear descriptions for both parameters. The description adds value by framing parameters as 'value and total' and providing an example, but since the schema already covers the syntax well, the additional semantics are helpful but not essential.

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 begins with a clear verb-resource pair 'Calculate what percentage one number is of another', followed by concrete examples. It explicitly distinguishes itself from siblings by mentioning chaining with percentage_increase and use with test_grade.

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 explicit usage contexts ('test scores, survey results, financial ratios, completion rates, and unit conversions') and suggests alternative tools for related tasks ('Chain with percentage_increase', 'use with test_grade'), guiding the agent on when to use this tool versus alternatives.

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