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SanctionScreen

by io.sanctionsscreen
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screen_counterparty

Read-onlyIdempotent

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": "..."}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dobNoDate of birth YYYY-MM-DD. Strongest corroborator (+15 score).
nameYesFull name of the person or entity to screen.
countryNoISO-3166 alpha-2 country code, e.g. RU.
request_idNoClient idempotency key; safe retries.
identifiersNoExtra identifiers to check against the matched record.
include_low_confidenceNoReturn fuzzy low-tier matches (default true). Never hide them.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnly and idempotent annotations, the description discloses detailed behavior: Unicode normalization and transliteration, token-order tolerance, never hiding low-confidence matches, exact confidence scoring math and tiers, disposition semantics, and a mandatory language rule. This is rich, behaviorally specific context an agent needs to interpret results correctly.

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?

The description is long but well organized with headings, an input list, output structure, confidence math, and three examples. There is some redundancy with the input schema's parameter descriptions, but the additional rationale and examples justify most of the length and make the tool easier to invoke correctly.

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?

With no output schema present, the description fully compensates by defining the output shape, disposition values, match object fields, confidence tiers, and the disclaimer behavior. It also includes worked examples that show clear, medium, and high-confidence outcomes, leaving little ambiguity about expected behavior.

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?

Although the schema already covers 100% of parameters, the description adds real meaning: dob is the strongest corroborator with a +15 score effect, include_low_confidence carries a compliance warning about hiding low matches, request_id is explained as an idempotency key safe for retries, and identifiers are tied to record corroboration. This goes well beyond the baseline of simply restating the schema.

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 and resource: 'Screen a counterparty name against the indexed sanctions lists (OFAC SDN, UK FCDO consolidated).' It clearly identifies the tool's domain and differentiates it from the sibling enrich_company by focusing on sanctions screening rather than company enrichment.

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: it is for sanctions-list screening, is informational only, and is not a legal verdict. However, it never explicitly contrasts itself with the sibling tool enrich_company or states when not to use screen_counterparty, so alternatives are somewhat implied rather than directly addressed.

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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