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validate_shipping_address

Validates and normalizes shipping addresses for fulfillment orders. Checks required fields, validates postal codes per country, and provides a normalized address to use when placing orders.

Instructions

Validate and normalize a shipping address for fulfillment orders.

        Args:
            street: Street address (e.g. "123 Main St").
            city: City name.
            country: ISO 3166-1 alpha-2 country code (e.g. "US", "GB", "DE").
            state: State/province (recommended for US addresses).
            postal_code: ZIP/postal code (validated per country format).

        Checks required fields, validates postal codes per country (US ZIP,
        Canadian postal, UK postcode), and returns warnings for missing optional
        fields.  Use the ``normalized`` address in the response when placing
        fulfillment orders.
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYes
stateNo
streetYes
countryYes
postal_codeNo
Behavior3/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions checking required fields, validating postal codes, and returning warnings and a normalized address. However, it does not explicitly state if the tool has side effects (e.g., writes to a database) or if it is read-only, leaving some ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise with two short paragraphs. The 'Args:' section is useful but slightly redundant since the schema already lists parameters. It could be shorter, but it remains clear and organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately explains what the tool returns (warnings and normalized address). It covers the main behaviors like validation of postal codes and identification of missing optional fields. The context is sufficient for a validation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds significant meaning to parameters beyond the input schema, which has 0% description coverage. For example, it specifies that country should be an ISO 3166-1 alpha-2 code and that state is recommended for US addresses. This compensates well for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it validates and normalizes a shipping address for fulfillment orders. The verb 'validate and normalize' combined with the resource 'shipping address' gives a specific and actionable purpose. It distinguishes from sibling tools like 'fulfillment_order' which creates orders, making this tool's role unique.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context by instructing to use the returned normalized address when placing fulfillment orders. It does not explicitly state when not to use or suggest alternatives, but the context is sufficient for most use cases.

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

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