mcp-legal-entity-resolver
Legal Entity Resolver MCP Server
MCP-сервер для актора Legal Entity Resolver от Mamba Labs на Apify.
Передайте ему домен компании — и он вернёт зарегистрированное юридическое лицо, стоящее за ним: юридическое название, номер компании, юрисдикцию, статус, LEI и VAT-номер. Одна плоская строка на домен, 24 поля, готово для Clay или CRM.
Установка
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" }
}
}
}Получите токен Apify на console.apify.com/account/integrations.
Related MCP server: enrich-company-mcp
Инструмент
resolve_legal_entity
На входе — домен компании, на выходе — зарегистрированное юридическое лицо, стоящее за ним.
Вход | Тип | Обязательно | Примечания |
| string | да | Один домен компании, например monzo.com. Протокол и путь отбрасываются. |
| string | нет | Пропускает поиск по домену и обращается напрямую к реестрам с этим именем. Используйте, когда у вас уже есть юридическое название и нужна только запись из реестра. |
| string | нет | Код страны ISO-2. |
| enum | нет |
|
| boolean | нет | Прогоняет любой VAT-номер, найденный на страницах компании, через сервис EU VIES и возвращает имя, которое VIES хранит для него, в качестве перекрёстной проверки с названием из реестра. По умолчанию |
| enum | нет |
|
Null — это продукт, а не пробел
Поисковые конечные точки реестров нечёткие и всегда возвращают что-то. Если брать верхний результат поиска, вы чаще всего получите уверенно неверный номер компании. Этот актор принимает совпадение только тогда, когда юридические названия идентичны после нормализации, поэтому примерно 6 доменов из 10 разрешаются, а не 10 из 10, и поэтому эти 6 заслуживают действий.
Прочитайте match_method, match_confidence и rejected_candidates, прежде чем действовать на основе совпадения. Строгость fuzzy — это исследовательский режим: на большинстве доменов он выдаст вам уверенно неверную компанию.
Запрашиваются три реестра: UK Companies House, GLEIF и SEC EDGAR.
Оплата
Вы платите за каждый разрешённый домен плюс небольшую плату за запуск актора. Кэшированные результаты хранятся 90 дней для найденной компании и 7 дней для null.
Цены указаны на странице актора на Apify. Запуск этого сервера расходует кредиты Apify.
Что этот сервер делает и чего не делает
Это тонкий клиент для актора Apify. Он передаёт ваш ввод как есть и возвращает вывод актора без изменений. Всё описанное выше поведение реализовано в акторе, а не здесь.
Это не база данных компаний и не кредитный или рисковый продукт. Он не оценивает компании, не присваивает им рейтинги и не говорит, стоит ли с ними работать. Он отвечает на один вопрос: какое зарегистрированное юридическое лицо стоит за этим доменом.
Ошибки отображаются, а не проглатываются. Неверный ввод, неверный токен, исчерпанный баланс, таймаут или запуск, возвращающий что-либо кроме dataset, — всё это возвращается как явная ошибка инструмента, а не как пустой результат.
Исходный код
Актор доступен в Apify Store. Эта обёртка распространяется по лицензии MIT.
Создано Mamba Labs.
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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