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Sausage Trust

by com.professorsausages.x402

Phone number validation

phone_check

Validate and normalize a phone number: E.164 form, line type, region, and a verdict. Pure CPU, no third-party lookup. Paid: call without x_payment to receive this call's exact terms (amount, asset, network), sign them, then call again with x_payment. The free pricing tool lists every price at once.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
numberYesThe number, ideally in E.164.
regionNoISO-3166 region hint for national-format numbers.
x_paymentNoOptional signed x402 payment payload (base64, what the X-PAYMENT header carries). Omit to receive the exact payment terms; sign them (e.g. @x402/fetch) and call again with this argument to settle and get the data.

TDQS

A4.7/5.0
Behavior5/5

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

No annotations are present, so the description carries full burden. It explicitly says 'Paid:' and explains the two-step payment workflow with x_payment, plus 'Pure CPU, no third-party lookup.' This discloses cost, execution environment, and external dependency behavior clearly.

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

Conciseness5/5

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

The description is three sentences, each earning its place: purpose/outputs, execution model, and payment workflow. No redundant wording or filler.

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

Completeness5/5

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

Despite no output schema, the description names all output types (E.164, line type, region, verdict). It also explains the payment behavior and the free `pricing` alternative, giving an agent enough context to invoke the tool correctly.

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?

Schema coverage is 100%, so baseline is 3. The description adds significant meaning to `x_payment` by explaining the exact workflow (omit for terms, sign, then call again). It also implies the `number` parameter's validated output (E.164 form), but does not detail `region` beyond schema.

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 opens with 'Validate and normalize a phone number,' a specific verb and resource, and lists concrete outputs (E.164 form, line type, region, verdict). This clearly distinguishes it from siblings like email_check or domain_intel.

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 does not explicitly state when-not-to-use, but it says 'The free `pricing` tool lists every price at once,' directing pricing-related queries to an alternative. The purpose sentence implies when to use the tool, but lacks explicit exclusion of alternative validation tools.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct resource type (crypto address, domain, email, IBAN, phone, URL, vessel, entity name) or a distinct purpose (pricing, data discovery, data request). Overlapping sanctions tools are clearly differentiated by target: address_screen for addresses, sanctions_screen for names, vessel_screen for vessels, and sanctions_entity for detailed records after screening.

Naming Consistency3/5

Most data-check tools follow a consistent object_verb pattern (e.g., address_screen, email_check, phone_check). However, find_data and request_data invert the order, domain_intel uses a noun instead of a verb, and pricing stands alone as a gerund, creating mixed conventions.

Tool Count5/5

12 tools is well within the ideal range for a data-screening server. Each tool covers a distinct verification task, and the additional meta tools (pricing, find_data, request_data) are useful entry points without bloating the core purpose.

Completeness4/5

The server covers a comprehensive set of screening and validation tasks across sanctions, domain, email, phone, IBAN, and URL. It includes a discovery tool (find_data) and a suggestion tool (request_data) to fill gaps, though an IP checker or company registry lookup could be considered minor omissions.

Resources