WritersLogic verifiable AI agents

locale-number

Parse locale-formatted money text (1.234,56, 1,00,000, accounting parentheses) without guessing — Parses up to 100 number or money strings such as "1.234,56", "1,00,000", "(1,234.50)", "￥1,000" or "CHF 1'234.50" into canonical decimal strings, for an agent reading amounts from documents, emails or tables. With a BCP 47 locale it applies that locale's CLDR separators (read from Intl.NumberFormat); without one it lists every valid reading and an ambiguous string such as "1,234" returns unknown w

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "items": "<items>"
    },
    "name": "locale-number"
  }
}

Input Schema

Name	Required	Description	Default
`items`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 15, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and discloses important behavior: up to 100 items, CLDR separator application when a BCP 47 locale is supplied, listing every valid reading without a locale, and returning unknown for ambiguous strings like "1,234". It does not cover invalid-input handling or the full response structure.

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 purpose is front-loaded and the examples earn their place by illustrating locale formats. There is some redundancy in "Parse ... Parses," and the ending is truncated, but the description remains readable and appropriately sized.

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

Completeness3/5

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

The tool has nested input objects and no output schema, so the description should explain return values more fully. It states canonical decimal strings and ambiguity behavior, but it omits the output structure for multiple items, error behavior, and how currency resolution affects results.

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 reported as 0%, so the description must compensate. It explains the items array and locale behavior with examples, but it does not explain the currency field's role in resolving or contradicting symbols, nor does it clarify the text constraints.

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 uses a specific verb, "Parse," and a precise resource, "locale-formatted money text," with concrete examples like "1.234,56" and "(1,234.50)". It clearly states the output as canonical decimal strings, which distinguishes it from sibling tools such as number-to-words.

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?

It gives clear usage context: an agent reading amounts from documents, emails, or tables. It also explains the locale and no-locale paths, but it does not explicitly name alternatives or state when not to use this tool.

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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