resolve_company法人名（表記ゆれ・かな/カナ対応）から法人番号の候補と確度（0〜1＋根拠ラベル）を返します。データ源: 国税庁法人番号システムWeb-API。
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | 法人名（(株)等の略記・全半角・かな/カナ・旧字体の表記ゆれ可） | |
| limit | No | 返却する候補数の上限（既定10） | |
| address | No | 所在地で絞り込み: 都道府県コード2桁（JIS X 0401）または+市区町村コード3桁の計5桁 | |
| include_closed | No | 閉鎖法人を候補に含める（既定true） |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It adds valuable behavioral context by disclosing the data source (National Tax Agency Web-API) and the output structure (candidates with confidence and reason label). This goes beyond the schema, though it does not mention network side effects or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: the first states the core function and output, the second gives the data source. It is front-loaded and contains no filler, earning the maximum score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema and no annotations, so the description should compensate. It mentions candidates and confidence but does not detail the return format (e.g., JSON structure, array of objects) or edge cases like empty results or error behavior. It is adequate but incomplete for a fuzzy-matching tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with each parameter having a description. The description itself adds no parameter-specific detail beyond the schema—e.g., the mention of name variations duplicates the schema's name description. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool resolves corporate numbers from corporate names, returning candidates with confidence scores and reason labels. It specifies the input variations (kana/kanji, orthographic variants) and names the data source, making the purpose distinct from siblings like verify_company.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for fuzzy name-to-number resolution (due to mention of variants and confidence), but it does not explicitly state when to use this tool versus alternatives like verify_company or get_company_profile. There is no when-not/exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.