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Glama

会社名から法人を探す

bango_find_company
Read-onlyIdempotent

Find Japanese companies by name and return candidate corporate numbers for selection. Use it when you have a company name instead of a 13-digit corporate number; optionally narrow by prefecture.

Instructions

Find Japanese companies (法人) by name and return candidate corporate numbers (法人番号). Use this when the user gives a company name instead of a number, e.g. 「トヨタ自動車」「㈱サンプル 大阪」「sample shoji」. Name variants are absorbed automatically (株式会社/㈱/(株), full-width/half-width, spaces), so pass the name as the user wrote it; do not guess a 13-digit number. Narrow with prefecture when the user mentions a place. Results are ranked by score (0-1). If several candidates look plausible, ask the user which one they mean (show name and prefecture) instead of picking one. Then call bango_get_company with the chosen corporate_number for address and invoice status. Each call uses 1 API request from the user's Bango API quota.

Input Schema

NameRequiredDescriptionDefault
exactNotrue にすると表記ゆれを吸収したうえでの完全一致だけを返す。既定は部分一致(false)
limitNo返す候補の数(1〜20、既定5)
post_codeNo郵便番号(7桁、ハイフン可)で絞り込む
prefectureNo都道府県で絞り込む。「大阪」「大阪府」「27」「osaka」のどれでも可
company_nameYes会社名(一部でも可)。例: 「サンプル商事」「株式会社サンプル」

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive/openWorld, and the description adds real behavioral context on top: normalization behavior (株式会社/㈱/(株), full-width/half-width, spaces are absorbed), score-based ranking from 0-1, and a per-call quota cost of 1 API request. The quota note and ambiguity protocol are things no annotation conveys.

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 purpose, then usage, then the handoff to bango_get_company, with no filler sentences. It is dense with multiple clauses (normalization, ranking, disambiguation, quota) but every sentence carries operational value.

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?

There is no output schema, yet the description tells the agent what comes back (ranked candidates with name/prefecture and corporate numbers) and what to do next. For a read-only search tool with five fully documented parameters, nothing needed for correct invocation is missing.

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 the baseline is 3; the description earns extra by telling the agent how to fill company_name (pass the name as the user wrote it, variants are absorbed automatically) and when to apply prefecture. It does not add anything beyond the schema for exact, limit, or post_code.

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 ("Find Japanese companies (法人) by name") plus the return type (candidate corporate numbers). It is clearly distinguished from the sibling bango_get_company, which is named later as the by-number lookup step.

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

Usage Guidelines5/5

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

Gives an explicit trigger (user supplies a name instead of a number) with concrete examples, an explicit prohibition (do not guess a 13-digit number), a refinement rule (narrow with prefecture), and a follow-up routing rule (call bango_get_company with the chosen corporate_number). It also specifies what to do with ambiguous results — ask the user instead of picking.

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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