phone.normalize

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

No annotations are provided, so the description carries full burden. It explains that the tool normalizes and classifies phone numbers, listing output types. However, it does not disclose behavior for invalid numbers, error handling, or whether it is read-only (though safe to assume). This is adequate but not comprehensive.

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 a single, well-structured sentence that front-loads the core purpose and lists key outputs. Every element serves a purpose, with no extraneous text.

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 simplicity, no output schema, and no annotations, the description adequately covers what the tool does and returns. It lists format variants and type categories, providing sufficient context for an AI agent to understand and invoke the tool correctly.

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 description need not repeat parameter details. However, it adds value by explaining the classification (mobile, fixed_line, etc.) and the use of libphonenumber, which enriches understanding beyond the schema's simple parameter descriptions.

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 normalizes phone numbers to E.164 and classifies them using libphonenumber. It specifies return values (format variants, type, region), distinguishing it from any sibling tools, none of which handle phone normalization.

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 for normalizing and classifying phone numbers but provides no explicit guidance on when to use versus alternatives or when not to use it. There are no sibling tools with similar functionality, so the lack of exclusions is acceptable but not ideal.

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