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

With no annotations, the description must disclose behavioral traits. It only states 'Returns converted size' and does not explain edge cases, error handling, idempotency, or the conversion algorithm. The mention of a non-existent 'gender' parameter further undermines 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 concise with two sentences. The first sentence states the purpose, and the second lists inputs and hints at related tools. However, the inaccuracy (gender) slightly mars the efficiency.

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?

For a simple conversion tool, the description covers the main purpose and usage context. However, it omits details like output format (though an output schema exists) and fails to address the discrepancy with the input schema. It is adequate but not thorough.

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%, so the schema already documents all parameters. The description adds nothing of value; instead, it introduces a 'gender' parameter not present in the schema, creating confusion. This reduces the usefulness beyond what the schema already provides.

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 converts shoe sizes between EU, US, and UK systems and suggests use for online shopping. However, it inaccurately lists 'gender' as an input when the schema only includes value, from, and to. This slight mismatch could confuse an AI agent, but the core purpose is specific and distinguishable from siblings like calculate_shoe_size.

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 mentions using the tool for online shopping and directs to list_bundles for related calculators, providing basic context. However, it does not specify when to use this tool over alternatives (e.g., calculate_shoe_size_convert) or mention any prerequisites or exclusions.

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