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 core behavior—extracting email addresses from text—but does not reveal the output format, whether duplicates are removed, whether addresses are validated, or whether partial/obfuscated emails are handled. The behavior is clear at a high level but lacks detail an agent would benefit from.

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, stating the action in one efficient sentence. The prefixed 'Extract Emails:' is mildly redundant with the tool name and the following sentence, but it does not add meaningful bloat. It earns high concisese but loses points for being too thin to be genuinely helpful.

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 tool that claims to extract from text, the empty input schema is a serious gap—there is no documented way for an agent to supply the text. There is also no output schema or description of the return value, and no mention of edge cases or limits. Even though the task is simple, the absence of both input and output contracts makes this definition incomplete.

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 zero properties, so there are no parameters for the description to clarify; per the 0-parameter baseline, this dimension scores well. The phrase 'from text' hints at the expected input source, but the schema does not define how text is supplied, which is a gap handled more under contextual completeness.

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 function: extracting email addresses from text. It names a specific verb, resource, and source scope, so an agent knows the core action. It does not explicitly differentiate itself from siblings like x402-validate-email or x402-email-verify, but the purpose itself is unambiguous.

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 the tool should be used when email addresses need to be pulled out of free-form text, which provides basic usage context. However, it offers no guidance about when not to use it or which sibling tools would be better for validation, masking, or email intelligence tasks. The absence of exclusions makes it adequate but not strong.

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