mcp-legal-entity-resolver
Legal Entity Resolver MCP Server
Servidor MCP para el actor Legal Entity Resolver de Mamba Labs en Apify.
Dale un dominio de empresa y devuelve la entidad legal registrada que hay detrás: nombre legal, número de empresa, jurisdicción, estado, LEI y número de IVA. Una fila plana por dominio, 24 campos, lista para Clay o un CRM.
Instalación
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" }
}
}
}Obtén un token de Apify en console.apify.com/account/integrations.
Related MCP server: enrich-company-mcp
Herramienta
resolve_legal_entity
Dominio de empresa de entrada, entidad legal registrada de salida.
Entrada | Tipo | Obligatorio | Notas |
| string | sí | Un único dominio de empresa, por ejemplo monzo.com. Se eliminan el protocolo y la ruta. |
| string | no | Omite la búsqueda por dominio y va directamente a los registros con este nombre. Úsalo cuando ya tengas el nombre legal y solo quieras el registro. |
| string | no | Código de país ISO-2. |
| enum | no |
|
| boolean | no | Ejecuta cualquier número de IVA encontrado en las páginas propias de la empresa a través del servicio VIES de la UE y devuelve el nombre que VIES tiene para él, como verificación cruzada con el nombre del registro. Por defecto |
| enum | no |
|
Un nulo es el producto, no una carencia
Los endpoints de búsqueda de registros son difusos y siempre devuelven algo. Tomar el primer resultado de búsqueda te da un número de empresa equivocado con seguridad la mayoría de las veces. Este actor acepta una coincidencia solo cuando los nombres legales son idénticos tras la normalización, por eso aproximadamente 6 de cada 10 dominios se resuelven en lugar de 10 de cada 10, y por eso los 6 merecen la pena.
Lee match_method, match_confidence y rejected_candidates antes de actuar sobre una coincidencia. La estrictez fuzzy es un modo de investigación: te dará una empresa equivocada con seguridad en la mayoría de los dominios.
Se consultan tres registros: UK Companies House, GLEIF y SEC EDGAR.
Facturación
Se te cobra por dominio resuelto, más una pequeña tarifa de inicio del actor. Los resultados en caché son de 90 días para una empresa resuelta y 7 días para un nulo.
Los precios están en la página del actor en Apify. Ejecutar este servidor consume créditos de Apify.
Lo que este servidor hace y no hace
Es un cliente ligero para el actor de Apify. Pasa tu entrada directamente y devuelve la salida del actor sin cambios. Todo el comportamiento descrito anteriormente vive en el actor, no aquí.
Esto no es una base de datos de empresas ni un producto de crédito o riesgo. No puntúa empresas, no las califica ni te dice si debes comerciar con ellas. Responde una sola pregunta: qué entidad legal registrada se encuentra detrás de este dominio.
Los errores se muestran, nunca se ocultan. Una entrada no válida, un token no válido, un saldo agotado, un tiempo de espera agotado o una ejecución que devuelva algo distinto de un conjunto de datos se devuelven como un error explícito de la herramienta, no como un resultado vacío.
Fuente
El actor está en Apify Store. Este wrapper tiene licencia MIT.
Creado por 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
Resources
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