mcp-legal-entity-resolver
Legal Entity Resolver MCP Server
这是 Mamba Labs 在 Apify 上的 Legal Entity Resolver actor 的 MCP 服务器。
给它一个公司域名,它会返回其背后的注册法律实体:法定名称、公司编号、司法管辖区、状态、LEI 和增值税号。每个域名一行扁平数据,共 24 个字段,可直接用于 Clay 或 CRM。
安装
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" }
}
}
}在 console.apify.com/account/integrations 获取 Apify token。
Related MCP server: enrich-company-mcp
工具
resolve_legal_entity
输入公司域名,输出其背后的注册法律实体。
输入 | 类型 | 必填 | 备注 |
| string | 是 | 单个公司域名,例如 monzo.com。协议和路径会被去除。 |
| string | 否 | 跳过域名查找,直接使用此名称查询注册处。当你已经拥有法定名称,只想要注册记录时使用。 |
| string | 否 | ISO-2 国家代码。 |
| enum | 否 |
|
| boolean | 否 | 将公司自身页面上找到的任何增值税号通过欧盟 VIES 服务进行验证,并返回 VIES 持有的名称,作为与注册名称的交叉核对。默认值为 |
| enum | 否 |
|
null 是产品,不是缺陷
注册处搜索接口是模糊的,它们总会返回一些内容。直接采用排名第一的搜索结果,往往得到的是一串看似可信实则错误的公司编号。该 actor 仅在规范化后法定名称完全一致时才接受匹配,这就是为什么大约 10 个域名中有 6 个能解析成功,而不是 10 个全部成功,也是为什么这 6 个结果值得采取行动。
在对匹配结果采取行动之前,请阅读 match_method、match_confidence 和 rejected_candidates。fuzzy 严格度是一种研究模式:在大多数域名上,它都会给你一个看似可信实则错误的公司。
会查询三个注册处:UK Companies House、GLEIF 和 SEC EDGAR。
计费
按每个已解析的域名收费,外加少量 actor 启动费。缓存结果的有效期为:已解析的公司 90 天,null 7 天。
定价见 actor 的 Apify 页面。运行此服务器会消耗 Apify 积分。
此服务器做什么与不做什么
它是 Apify actor 的轻量客户端。它透传你的输入,并原样返回 actor 的输出。上述所有行为都存在于 actor 中,而不是这里。
这不是公司数据库,也不是信用或风险产品。它不会对公司评分、评级,也不会告诉你是否应与它们交易。它只回答一个问题:这个域名背后是哪个注册法律实体。
错误会被明确暴露,绝不会被吞掉。无效输入、无效 token、余额耗尽、超时,或运行返回的不是数据集,所有这些都会以明确的工具错误形式返回,而不是空结果。
来源
该 actor 位于 Apify Store。此包装器采用 MIT 许可证。
由 Mamba Labs 构建。
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.
Maintenance
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