Corporate Ownership Tree (GLEIF)

lei_ownership_tree

Find out who owns or controls a company: its parent chain, ultimate parent and subsidiaries. By LEI or company name, returns official GLEIF Level 2 data (CC0): direct and ultimate accounting-consolidation parents, reporting exceptions explaining a missing parent, and direct and ultimate subsidiaries with counts. Only entities with an LEI; this is accounting consolidation, not beneficial ownership. Use for KYB, credit or group-exposure checks. $0.02 per call, USDC on Base (x402).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "lei_ownership_tree"
  }
}

Input Schema

Name	Required	Description
`lei`	No	20-character LEI
`name`	No	Company name if no LEI
`children_limit`	No	Max direct subsidiaries listed (default 25)
`payment_signature`	No	x402 v2 PAYMENT-SIGNATURE header value. Omit to receive payment requirements.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 16, 2026

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does substantial work: it names the official source and license (GLEIF Level 2, CC0), states the coverage limitation (only entities with an LEI), discloses the cost model ($0.02 per call, USDC on Base, x402), and notes that reporting exceptions are returned when a parent is missing. It does not discuss rate limits, latency, or error behavior, keeping it short of a 5.

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

Conciseness4/5

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

Front-loaded with purpose, then data provenance, then the crucial caveat, then use cases and pricing — a sensible information ordering. It is slightly dense (provenance, exceptions, cost and payment rail all in one paragraph) but every sentence carries distinct information; nothing is padding.

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

Completeness4/5

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

For a four-parameter lookup tool with no output schema and no annotations, the description covers source, scope, limitations, return components and the payment flow, which is enough for an agent to decide and invoke correctly. The remaining gap is mechanical detail (rate limits, failure modes) rather than anything that would cause a wrong call.

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

Parameters3/5

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

Schema description coverage is 100%, so LEI, name, children_limit and payment_signature are already documented in the schema. The description only adds that lookup works 'by LEI or company name' and that subsidiary counts are returned, but says nothing about children_limit semantics or the payment-signature omit behavior beyond what the schema states. Baseline 3 is appropriate.

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?

States a specific verb and resource ('find out who owns or controls a company') and enumerates exactly what is returned: parent chain, ultimate parent, subsidiaries. It even draws a boundary against a commonly confused concept ('this is accounting consolidation, not beneficial ownership'), so the agent knows precisely what question this tool answers.

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

Usage Guidelines5/5

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

Explicit use cases are given ('Use for KYB, credit or group-exposure checks') plus exclusions that steer the agent away from misuse: only entities with an LEI, and accounting-consolidation rather than beneficial ownership. This is close to a full when-to-use/when-not matrix.

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

All Corporate Ownership Tree (GLEIF) tools Inspect

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