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LiveDataLink

by io.github.blackboxfoundry

beneficial_owner_screen

Read-onlyIdempotent

One-call KYB/AML ownership-chain sanctions screen: answers the real question 'is anyone in this company's ownership chain sanctioned?', not just 'is the top-level name on a list?'. Give a company name or a 20-character LEI; the tool maps the ownership structure via GLEIF (keyless CC0 reference data) - the subject entity, its ULTIMATE parent (top of the chain), and its reported direct subsidiaries, capped at about 15 entities so a broad group stays bounded - then SCREENS each entity in that set against the local sanctions/watchlist matcher (OFAC SDN / EU / UN / BIS), the same first-party matcher company_trust_check uses. Returns the mapped ownership structure, a per-entity CLEAR / HIT result (each hit naming the matched list entry, source, and score), and an overall verdict: CLEAR (no entity matched) vs HITS-FOUND (at least one entity in the chain matched). This catches a sanctioned parent or subsidiary that screening only the counterparty name would miss - the exposure beneficial-ownership rules target. Best-effort: if GLEIF hops fail the subject is still screened; if the sanctions binding is unavailable the screen is noted as unavailable, never silently passed. Only relationships an entity self-reports to GLEIF are shown. A name match is not proof of identity and must be cleared. Informational public-record synthesis, not legal, compliance, or sanctions-clearance advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
leiNoOptional exact 20-character LEI code to anchor the ownership chain 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

A4.5/5.0
Behavior5/5

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

The description goes well beyond the readOnly/openWorld/idempotent annotations by disclosing best-effort behavior, failure handling ('if GLEIF hops fail the subject is still screened'), the cap on entity count, reliance on self-reported GLEIF relationships, the possible verdicts, and the disclaimer that a name match is not proof of identity. This gives the agent a rich, accurate model of side effects and limitations.

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 earns its place: it front-loads the core purpose, then explains the mechanism, output semantics, failure modes, and legal caveat. There is no redundant filler, and the critical distinction from top-level-only screening appears immediately.

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?

Even without an output schema, the description explains the returned ownership structure, per-entity CLEAR/HIT results with matched list entry/source/score, and the overall verdict. It also covers data sources, caps, fallback behavior, and the non-advisory nature of the tool, making it sufficiently complete for an agent to call and interpret results correctly.

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?

The input schema already covers parameter semantics fully: 'lei' is described as an optional exact 20-character code that overrides a name search, and 'company' is described as a name or LEI. The description mostly restates this in prose without adding meaningfully new parameter-level detail, so the baseline of 3 for high schema coverage applies.

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 action and resource: 'One-call KYB/AML ownership-chain sanctions screen' that checks the ownership chain, not just the top-level name. It clearly distinguishes itself from simple name-based screening by stating 'not just the top-level name' and references company_trust_check's matcher, making the tool's unique scope evident.

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?

The description gives clear context on when to use this tool: when the real question is whether anyone in the ownership chain is sanctioned, not just the counterparty. It also provides input guidance ('Give a company name or a 20-character LEI') and caveats like the 15-entity cap. It does not explicitly name alternative tools or state when not to use it, so it stops short of a 5.

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.

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