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Phone number check

util_phone

Checks one phone number offline: normalises it to E.164, works out its country (including the twelve calling codes shared by several countries, such as +1 US/Canada/Caribbean) and validates the national number length against that country's numbering plan. Returns the tel: URI and the national dialling form. Numbering data from Google's libphonenumber. No network call and no API key, so it cannot fail because a third party is down. $0.005 per call, paid over x402 (USDC).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
phoneYesThe phone number, in any usual notation: +34 612 34 56 78, 0034612345678, (612) 345-678.
countryNoOptional ISO 3166-1 alpha-2 country, e.g. ES. Only used when the number carries no country code.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior5/5

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

With no annotations at all, the description carries the full burden and does so well: it declares the tool is offline, requires no network or API key, therefore cannot fail due to third-party downtime, and — critically for an agent — costs $0.005 per call settled over x402 in USDC. Cost and payment mechanism are behavioural facts an agent cannot get anywhere else.

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?

Front-loaded with the core action, and each subsequent clause (offline, no key, cost, data source) adds decision-relevant information rather than filler. The single long sentence is dense but every element earns its place; only minor tightening is possible.

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?

With no output schema and no annotations, the description compensates by naming the returned values (tel: URI, national dialling form), the data source, and the pricing model. It omits error behaviour for an invalid number (e.g., whether a failed check is still billed) and any rate-limit detail, which are the remaining gaps for a paid tool.

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

Parameters3/5

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

Schema description coverage is 100%, so both parameters are already documented, including the rule that 'country' only applies when the number carries no country code. The description's discussion of shared calling codes (+1) explains why country resolution matters but does not add syntax or format detail beyond the schema, so the baseline of 3 applies.

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?

States a specific verb and resource ('Checks one phone number'), then enumerates the three concrete operations: E.164 normalisation, country resolution, and national-length validation. It also discloses the return payload (tel: URI and national dialling form), which clearly separates it from siblings like util_iban or util_vat.

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

Usage Guidelines3/5

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

The phrase 'one phone number' implies scope and the offline/no-API-key framing hints at when this is preferable to a networked lookup, but there is no explicit when-to-use statement and no sibling is named as an alternative. Usage is inferable rather than stated.

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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