mcp-legal-entity-resolver
Legal Entity Resolver MCP Server
Apify의 Mamba Labs Legal Entity Resolver 액터를 위한 MCP 서버입니다.
회사 도메인을 입력하면 그 뒤에 있는 등록된 법인을 반환합니다: 법인명, 회사 번호, 관할권, 상태, LEI, VAT 번호. 도메인당 하나의 평면 행으로 24개 필드를 제공하며, Clay 또는 CRM에 바로 사용할 수 있습니다.
설치
npx -y @mambalabsdev/mcp-legal-entity-resolverClaude Desktop
{
"mcpServers": {
"mamba-legal-entity-resolver": {
"command": "npx",
"args": ["-y", "@mambalabsdev/mcp-legal-entity-resolver"],
"env": { "APIFY_TOKEN": "your-apify-token" }
}
}
}Apify 토큰은 console.apify.com/account/integrations에서 받으세요.
Related MCP server: enrich-company-mcp
도구
resolve_legal_entity
회사 도메인을 입력하면 그 뒤에 있는 등록된 법인을 반환합니다.
입력 | 유형 | 필수 | 설명 |
| string | 예 | 단일 회사 도메인(예: monzo.com). 프로토콜과 경로는 제거됩니다. |
| string | 아니요 | 도메인 조회를 건너뛰고 이 이름으로 바로 등록부를 조회합니다. 이미 법인명을 알고 있고 등록부 기록만 필요한 경우 사용합니다. |
| string | 아니요 | ISO-2 국가 코드. |
| enum | 아니요 |
|
| boolean | 아니요 | 회사 자체 페이지에서 찾은 VAT 번호를 EU VIES 서비스로 검증하고 VIES가 보유한 이름을 반환하여 등록부 이름과 대조합니다. 기본값은 |
| enum | 아니요 |
|
null은 공백이 아니라 결과물입니다
등록부 검색 엔드포인트는 fuzzy 방식이라 항상 무언가를 반환합니다. 상위 검색 결과를 그대로 사용하면 십중팔구 자신 있게 틀린 회사 번호를 얻게 됩니다. 이 액터는 정규화 후 법인명이 동일한 경우에만 일치로 간주합니다. 그래서 10개 중 10개가 아니라 대략 10개 중 6개 도메인만 식별되며, 그 6개가 행동할 가치가 있는 이유입니다.
일치 결과를 활용하기 전에 match_method, match_confidence, rejected_candidates를 읽으세요. fuzzy 엄격도는 연구 모드입니다. 대부분의 도메인에서 자신 있게 틀린 회사를 건네줄 것입니다.
조회되는 등록부는 UK Companies House, GLEIF, SEC EDGAR 세 곳입니다.
요금
식별된 도메인당 요금이 부과되며, 소액의 액터 시작 수수료가 추가됩니다. 캐시된 결과는 식별된 회사의 경우 90일, null의 경우 7일입니다.
가격은 액터의 Apify 페이지에서 확인할 수 있습니다. 이 서버를 실행하면 Apify 크레딧이 소모됩니다.
이 서버가 하는 일과 하지 않는 일
이 서버는 Apify 액터의 얇은 클라이언트입니다. 입력을 그대로 전달하고 액터의 출력을 변경 없이 반환합니다. 위에서 설명한 모든 동작은 여기가 아니라 액터에 있습니다.
이 서버는 회사 데이터베이스도, 신용·리스크 상품도 아닙니다. 회사를 평가하거나 등급을 매기거나 거래 여부를 알려주지 않습니다. 단 하나의 질문에 답할 뿐입니다: 이 도메인 뒤에 있는 등록된 법인은 무엇인가?
오류는 숨기지 않고 표면화됩니다. 잘못된 입력, 유효하지 않은 토큰, 잔액 부족, 시간 초과, 또는 데이터셋이 아닌 다른 것을 반환하는 실행은 모두 빈 결과 대신 명시적인 도구 오류로 반환됩니다.
소스
액터는 Apify Store에 있습니다. 이 래퍼는 MIT 라이선스입니다.
Mamba Labs가 만들었습니다.
Available Tools
1 toolresolve_legal_entityResolve Legal EntityARead-onlyIdempotent
Give it a company domain and it returns the registered legal entity behind it: legal name, company number, jurisdiction, status, entity type, LEI and VAT number, as one flat row with a full audit trail of what was rejected and why. Three registers are queried: UK Companies House, GLEIF and SEC EDGAR. Register search endpoints are fuzzy and always return something, so by default a record is accepted only when the normalized legal names are identical. That is why roughly 6 domains in 10 resolve rather than 10 in 10, and why a null here is a trustworthy answer rather than a gap. Read match_method, match_confidence and rejected_candidates before acting on a match. Setting match_strictness to fuzzy will hand you a confidently wrong company on most domains and should be treated as a research mode, not a default. This is not a company database and not a credit or risk product. Requires an APIFY_TOKEN and consumes Apify credits. Read only.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | A single company domain, for example monzo.com. Protocol and path are stripped. | |
| skipCache | No | false uses the cache: 90 days for a resolved company, 7 days for a null. true forces a fresh look. Default: "false". | |
| validate_vat | No | Runs any VAT number found on the company's own pages through the EU VIES service and returns the name VIES holds for it, as a cross-check against the register name. Default: true. | |
| legal_name_hint | No | Skips the domain lookup and goes straight to the registers with this name. Use it when you already have the legal name and just want the register record. | |
| match_strictness | No | exact accepts a register record only when the normalized legal names are equal, which is the default and the recommendation. fuzzy returns the best scoring candidate with a confidence below 100 and a warning in rejected_candidates. Register search is fuzzy and always returns something, so fuzzy mode will hand you a confidently wrong company on most domains. Default: "exact". | |
| jurisdiction_hint | No | ISO-2 country code, for example GB or US. Narrows which registers are queried and cuts latency. Leave empty to query every register. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description significantly expands on the annotations. Annotations only state readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds key behavioral details: 'Register search endpoints are fuzzy and always return something,' 'a record is accepted only when the normalized legal names are identical,' and the resulting resolution rate ('roughly 6 domains in 10 resolve'). It also discloses credit consumption, which is not in the annotations. This provides a thorough behavioral profile beyond the structured fields.
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?
The description is longer than average but every sentence earns its place. It is front-loaded with the core purpose, then layers behavioral context, caveats, exclusions, and requirements. There is no fluff; even the redundancy about fuzzy mode emphasizes a critical warning. The structure is logical and easy to scan.
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?
With no output schema, the description carries the full burden of explaining return values, and it does: it lists the returned fields (legal name, company number, jurisdiction, status, entity type, LEI, VAT number), the audit trail, and the match attributes. It also covers failure modes (null results), the reason behind them, and the registers queried. This is a complete picture for a tool of this complexity.
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 description coverage is 100%, so all six parameters are already well-documented in the schema. The description's mention of match_strictness and fuzzy mode largely repeats the schema's own warning ('fuzzy mode will hand you a confidently wrong company on most domains'). Since the schema already carries the heavy lifting and the description adds minimal additional parameter-level meaning, the baseline of 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's purpose with a specific verb and resource: 'Give it a company domain and it returns the registered legal entity behind it: legal name, company number, jurisdiction, status, entity type, LEI and VAT number.' It also distinguishes itself from non-purposes by saying 'This is not a company database and not a credit or risk product.' Although there are no sibling tools to differentiate from, this goes beyond a basic definition by listing exact output fields.
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 provides explicit usage guidance: it explains when to trust null results ('a null here is a trustworthy answer rather than a gap'), warns against using fuzzy mode in production ('should be treated as a research mode, not a default'), and tells users to 'Read match_method, match_confidence and rejected_candidates before acting on a match.' It also specifies prerequisites ('Requires an APIFY_TOKEN and consumes Apify credits') and excludes specific use cases, offering clear context and exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of confusion or misselection. The tool's purpose is clearly defined and distinct.
The tool name 'resolve_legal_entity' follows a clear verb_noun pattern, which is consistent and predictable even as a single tool.
The server is highly specialized, and a single complex tool is reasonable for its narrow purpose. While slightly under the typical 3-15 range, the tool's depth justifies the count.
The tool provides a comprehensive resolution workflow with audit trail, matching controls, and clear output. For its stated domain, there are no obvious missing capabilities.
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