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by io.github.blackboxfoundry

corporate_ownership_tree

Read-onlyIdempotent

One-call FULL corporate-ownership tree for a company from GLEIF (keyless CC0 reference data). Give a company name or a 20-character LEI; the tool resolves the entity, climbs to its ULTIMATE parent (top of the ownership chain), then walks DOWN from that root to list the reported direct subsidiaries plus one level of grand-subsidiaries, each with its LEI, jurisdiction, and status (entity ACTIVE/INACTIVE + LEI registration ISSUED/LAPSED). The queried entity is marked in the tree so you can see where it sits. Distinct from resolve_entity, which returns the ultimate parent plus a subsidiary COUNT only: this returns the actual subsidiary LIST/tree for M&A, diligence, and counterparty mapping. The tree is capped (about 25 nodes) so a broad conglomerate stays bounded, with an 'and N more' note where GLEIF reports additional subsidiaries. Only relationships an entity self-reports to GLEIF are shown, so coverage varies by company. A failing hierarchy hop is noted, not fatal. Informational public-record synthesis; verify against GLEIF before relying on it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
leiNoOptional exact 20-character LEI code to anchor the tree directly (e.g. '5493006MHB84DD0ZWV18'). Overrides a name search.
companyNoCompany / organization name (e.g. 'Alphabet Inc.', 'JPMorgan') or a 20-character LEI code. Provide this or 'lei'.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (read-only, idempotent, open-world, non-destructive), the description discloses crucial behaviors: the tree is capped at about 25 nodes with an 'and N more' note, only self-reported GLEIF relationships are shown, and failing hierarchy hops are non-fatal. This is exactly the kind of behavioral context an agent needs.

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 dense but every sentence earns its place: purpose, input, traversal logic, returned fields, sibling distinction, cap/limitations, and verification caveat. It is well structured and front-loaded with the core capability.

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?

For a tool with no output schema, the description thoroughly explains what the result contains: subsidiaries with LEI, jurisdiction, status, the marked queried entity, the tree cap, and the note for additional subsidiaries. It also covers limitations and verification, so nothing critical is missing for correct invocation.

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 the two parameters are fully documented in the schema. The description adds a little by saying 'Give a company name or a 20-character LEI,' but it does not meaningfully extend the schema's parameter explanations, so the baseline of 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?

The description opens with 'One-call FULL corporate-ownership tree' and specifies the resource (GLEIF ownership data) and the operation (resolve, climb to ultimate parent, walk down to list subsidiaries). It explicitly distinguishes itself from resolve_entity, so an agent can tell exactly what this tool produces versus that sibling.

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?

It names resolve_entity as the alternative and explains the difference: resolve_entity gives ultimate parent plus a subsidiary COUNT, while this tool returns the actual subsidiary LIST/tree. It also names concrete use cases—M&A, diligence, and counterparty mapping—making when-to-use 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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TDQS

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

Resources