SanctionScreen

Screen a counterparty name against the indexed sanctions lists (OFAC SDN, UK FCDO consolidated). What it does: normalizes the query (Unicode NFKD fold, punctuation strip, lowercase, Cyrillic/Greek transliteration; tolerant to token order e.g. "Putin Vladimir" vs "Vladimir Putin"), looks it up in the sanctions index, and returns EVERY match at low confidence or above with a per-match citation. Low-confidence matches are never hidden. Inputs: - name (required, string): full name of the person or entity. - country (optional, ISO-3166 alpha-2, e.g. "RU"): corroborates nationality/citizenship identifiers. - dob (optional, "YYYY-MM-DD"): a dob match adds +15 to the score — the strongest corroborator. - identifiers (optional, [{type, value}]): extra identifiers to check against the record, e.g. [{"type":"passport","value":"719023441"}]. - request_id (optional, string): idempotency key; safe to retry. - include_low_confidence (optional, boolean, default true): fuzzy matches (Jaro-Winkler >= 0.92) land in the low tier. Keep true — hiding low matches is a compliance liability. Output: {screening_id, disposition, matches[], disclaimer, generated_at}. - disposition: "clear" (no matches) | "potential_match" (any medium/low match) | "review_advised" (any high-confidence match — human review required before any adverse action). - Each match: {list, list_date, record_uid, matched_name, name_kind (primary|alias_strong|alias_weak), confidence (high|medium|low), confidence_score (0-100), matched_fields, citation {source, url, accessed_at}}. - Confidence math: exact primary 80 / alias_strong 70 / alias_weak 55; dob match +15. Tiers: high >= 85, medium >= 60, low >= 30. Fuzzy (JW >= 0.92) -> low tier, score 30-55. - LANGUAGE RULE: report "potential match", never "sanctioned". Screening is informational only — not a legal verdict. Every response carries the fixed disclaimer. EXAMPLE 1 — listed individual with corroborating dob: Input: {"name": "Vladimir Putin", "dob": "1952-10-07", "request_id": "demo-001"} Output: {"screening_id": "cs_scr_...", "disposition": "review_advised", "matches": [{"list": "OFAC_SDN", "list_date": "2026-09-23", "record_uid": "35096", "matched_name": "putin vladimir", "name_kind": "alias_strong", "confidence": "high", "confidence_score": 85, "matched_fields": ["name", "dob"], "citation": {"source": "U.S. Treasury OFAC SDN List", "url": "https://sanctionssearch.ofac.treas.gov/", "accessed_at": "2026-09-26T...Z"}}], "disclaimer": "Informational only — not a legal verdict. Not a substitute for due diligence. Human review required before any adverse action.", "generated_at": "2026-09-26T...Z"} EXAMPLE 2 — same person, no dob (exact alias match, medium): Input: {"name": "Vladimir Putin"} Output: {"screening_id": "cs_scr_...", "disposition": "potential_match", "matches": [{"list": "OFAC_SDN", "list_date": "2026-09-23", "record_uid": "35096", "matched_name": "putin vladimir", "name_kind": "alias_strong", "confidence": "medium", "confidence_score": 70, "matched_fields": ["name"], "citation": {...}}], "disclaimer": "...", "generated_at": "..."} EXAMPLE 3 — clear name: Input: {"name": "John Smith"} Output: {"screening_id": "cs_scr_...", "disposition": "clear", "matches": [], "disclaimer": "...", "generated_at": "..."}

Resolve a company domain or legal name to structured company facts via the free public GLEIF LEI registry. Inputs: {domain? e.g. "apple.com", name? e.g. "Apple Inc.", request_id? (idempotency)}. At least one of domain/name is required. v1 boundaries (returned honestly, never invented): - GLEIF has no domain index: domain-only lookups are best-effort (registrable-name candidates are tried). Provide the legal company name for reliable resolution. - GLEIF does not publish industry, industry_code, size_band, or employee_range: those come back null, each with a sources[] entry documenting the gap. - founded is the legal-entity creation date (GLEIF publishes no true founding date). - Unresolvable input -> tool error with code UNRESOLVABLE. Output: {company_id, legal_name, domain, industry, industry_code, size_band, employee_range, hq: {country, city}, founded, sources: [{field, source, url}], confidence, generated_at}. EXAMPLE 1: Input: {"domain": "apple.com", "request_id": "demo-e1"} Output: {"company_id": "cs_co_...", "legal_name": "Apple Inc.", "domain": "apple.com", "industry": null, "industry_code": null, "size_band": null, "employee_range": null, "hq": {"country": "US", "city": "Cupertino"}, "founded": "1980-12-12", "sources": [{"field": "legal_name", "source": "GLEIF LEI (api.gleif.org)", "url": "https://api.gleif.org/api/v1/lei-records/..."}, {"field": "industry", "source": "GLEIF LEI — field not published by GLEIF (null by design in v1)", "url": "https://api.gleif.org/api/v1/lei-records/..."}], "confidence": "high", "generated_at": "2026-09-26T...Z"} EXAMPLE 2 — unresolvable: Input: {"name": "Nonexistent Widgets Pty Ltd ZZ"} Tool error: {"code": "UNRESOLVABLE", "message": "No GLEIF LEI record matched ..."}