jp-number-grounding

Extract and normalize numbers, amounts, dates and times from Japanese text

jp_number_extract

Read-onlyIdempotent

Extracts every quantity from Japanese text and returns its normalized value, covering kanji numerals, 万/億 units, dates, times, rates, counters, and era years while skipping number-like words.

Instructions

List every quantity in a Japanese text with its normalized value: kanji numerals, 万/億 units, full-width digits, Japanese era years (令和/平成/昭和, R7), dates, 午前/午後 times, 割/分 rates, counters (人, 名, 件, 社, 回, か月 ...). Skips words that only look like numbers (一宮, 統一, 一部, 万一). Returns facts with kind, text, normalized, value, unit, approx, issues.

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": {
      "text": "<text>"
    },
    "name": "jp_number_extract"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 5, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuine value beyond them: it discloses the false-positive exclusion policy (一宮, 統一, 一部, 万一) and enumerates the returned fact fields, which matters because no output schema exists.

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?

Two sentences, front-loaded with the core action and normalized-value output, followed by the coverage list and the false-positive exclusion. The parenthetical enumerations are dense but every element adds discriminating information, and nothing is redundant with the schema or annotations.

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 parameter descriptions, the description does what it must: it names the returned fact fields (kind, text, normalized, value, unit, approx, issues) and defines scope. It stops short of explaining error behavior or ambiguous-number handling (the 'approx' and 'issues' fields are named but not characterized).

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?

There is a single required parameter with 0% schema description coverage, so the description carries the full burden of explaining it. It does so only implicitly through the phrase 'in a Japanese text,' without stating format expectations (plain text vs. markup), length limits, or whether a whole document is acceptable. Adequate but not compensating for the coverage gap.

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 a concrete verb and resource ('List every quantity in a Japanese text with its normalized value') and then enumerates the exact token classes covered: kanji numerals, 万/億 units, full-width digits, era years, dates, 午前/午後 times, 割/分 rates, and counters. That level of specificity makes it easy to distinguish from the sibling jp_number_grounding_check, which by name implies verification rather than extraction.

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?

Use is strongly implied by the resource description (feed it Japanese text, get quantities back), and the 'skips words that only look like numbers' clause clarifies a boundary case. However, there is no explicit when-to-use/when-not-to-use statement and no mention of the jp_number_grounding_check alternative, so an agent must infer routing.

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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