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ARM Verify — Entity Intelligence API

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map_ownership

Trace the corporate ownership chain above a company and identify the natural persons who ultimately control it. RETURNS JSON with: ownership_chain (array of parent entities, each with name, relationship, jurisdiction and is_ubo_candidate), natural_persons (array of identified beneficial owners, each with name, nationality and natures_of_control such as "ownership-of-shares-75-to-100-percent"), lei of the subject entity, complexity ("SIMPLE", "COMPLEX" or "UNKNOWN"), data_confidence ("HIGH", "MEDIUM" or "LOW") and notes describing the chain depth found. SOURCES: GLEIF Relationship API for LEI parent-child records, Companies House PSC (People with Significant Control) Register for UK entities, and the OpenCorporates corporate network. USE WHEN beneficial-ownership disclosure is required under AML or KYC rules, or to detect ownership by a sanctioned parent. Called via MCP — usage is logged but not billed. For metered billing use the REST API with an API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoRequested ownership-chain depth, 1 to 3 (1 = immediate parent only, 3 = up to three levels up). Example: 2. Accepted for forward compatibility but NOT yet enforced — the current implementation returns the full chain it can discover from the available sources regardless of this value.
entity_nameYesFull registered company name of the subsidiary or subject company whose owners you want. Example: "Volkswagen AG".
jurisdictionYesISO 3166-1 alpha-2 country code of incorporation, uppercase. Examples: "GB", "DE", "US", "NL". UK entities ("GB") return the richest data because the PSC Register is queried in addition to GLEIF.
registration_numberNoOptional national company number. Strongly recommended for UK entities, where it makes the PSC Register lookup exact instead of name-based. Example: "00445790" (GB, 8 digits). Omit if unknown.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changed
    • changedInput schema / properties / depth / description
      Previous value: -"Ownership chain depth (1-3)"New value: +"Requested ownership-chain depth, 1 to 3 (1 = immediate parent only, 3 = up to three levels up). Example: 2. Accepted for forward compatibility but NOT yet enforced — the current implementation returns the full chain it can discover from the available sources regardless of this value."
    • changedInput schema / properties / depth / type
      Previous value: -"number"New value: +"integer"
    • changedInput schema / properties / entity_name / description
      Previous value: -"Company name"New value: +"Full registered company name of the subsidiary or subject company whose owners you want. Example: \"Volkswagen AG\"."
    • changedInput schema / properties / jurisdiction / description
      Previous value: -"ISO 2-letter country code (e.g. GB, DE, US)"New value: +"ISO 3166-1 alpha-2 country code of incorporation, uppercase. Examples: \"GB\", \"DE\", \"US\", \"NL\". UK entities (\"GB\") return the richest data because the PSC Register is queried in addition to GLEIF."
    • changedInput schema / properties / registration_number / description
      Previous value: -"Company registration number — improves UK PSC lookup accuracy"New value: +"Optional national company number. Strongly recommended for UK entities, where it makes the PSC Register lookup exact instead of name-based. Example: \"00445790\" (GB, 8 digits). Omit if unknown."
  2. Changed4 schema fields changed
    • addedInput schema / properties / depth / description
      Added value: +"Ownership chain depth (1-3)"
    • addedInput schema / properties / entity_name / description
      Added value: +"Company name"
    • addedInput schema / properties / jurisdiction / description
      Added value: +"ISO 2-letter country code (e.g. GB, DE, US)"
    • addedInput schema / properties / registration_number / description
      Added value: +"Company registration number — improves UK PSC lookup accuracy"
  3. Changed4 schema fields changed
    • removedInput schema / properties / depth / description
      Removed value: -"Ownership chain depth (1-3)"
    • removedInput schema / properties / entity_name / description
      Removed value: -"Company name"
    • removedInput schema / properties / jurisdiction / description
      Removed value: -"ISO 2-letter country code (e.g. GB, DE, US)"
    • removedInput schema / properties / registration_number / description
      Removed value: -"Company registration number — improves UK PSC lookup accuracy"
  4. Changed2 schema fields changed
    • changedInput schema / properties / jurisdiction / description
      Previous value: -"ISO 2-letter country code"New value: +"ISO 2-letter country code (e.g. GB, DE, US)"
    • addedInput schema / properties / registration_number
      Added value: +{
+  "description": "Company registration number — improves UK PSC lookup accuracy",
+  "type": "string"
+}
  5. First observed

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral disclosure burden, and it does so thoroughly: it details the exact JSON return fields, names the data sources (GLEIF, Companies House PSC, OpenCorporates), and candidly warns that the depth parameter is accepted but not yet enforced. It also adds operational context around logging and billing, so an agent can predict the tool's behavior accurately.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every sentence carries distinct information: purpose, return shape, sources, use cases, and operational constraints. It is front-loaded with the core purpose and then flows logically into output and caveats, making it easy for an agent to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema and no annotations, but the description compensates by enumerating the return object fields, data sources, use cases, and critical behavioral caveats. An agent has enough context to invoke the tool and interpret its results correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers all four parameters with 100% coverage, so the baseline is 3, but the description adds meaning beyond the schema: it notes that depth is ignored in the current implementation, that UK (GB) entities return the richest data via PSC, and that registration_number makes UK lookups exact. This materially improves correct invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb ('Trace') and a concrete resource ('the corporate ownership chain above a company'), and clarifies the end goal (identifying natural persons who ultimately control it). This distinguishes it from sibling tools like score_risk and screen_sanctions, which target risk scoring and sanctions matching rather than ownership mapping.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states when to use the tool: 'USE WHEN beneficial-ownership disclosure is required under AML or KYC rules, or to detect ownership by a sanctioned parent.' It does not name sibling alternatives or state when not to use them, so it stops short of full routing guidance; nevertheless the use cases are clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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