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fairquery

fairquery-sanctions-screener

Official

Screen names against sanctions lists

screen_names
Read-onlyIdempotent

Fuzzy-match person and organisation names against OFAC, EU, UK and UN sanctions lists to screen for potential matches during KYC/KYB onboarding or bulk re-screening. Returns match status and scores.

Instructions

WHAT: Fuzzy-match people or organisation names against the OFAC SDN, OFAC Consolidated (non-SDN), EU, UK and UN sanctions lists, from the official sources (cached in your Apify account for at most 6 h, otherwise re-downloaded; each row reports list publication dates). Order-, case- and diacritics-insensitive; optional country and date-of-birth filters per name. WHEN TO USE: KYC/KYB onboarding, vendor or counterparty checks, bulk re-screening of customer lists. Put all names into one call (lists are downloaded once per run). WHEN NOT TO USE: to see which lists/authorities are covered or to cite sources, call list_sources (free). Not a PEP/adverse-media check, not a certified screening service. COST: paid. Runs Apify Actor fairquery/sanctions-watchlist-screener with your APIFY_TOKEN; pay-per-event "name-screened": $0.003 per name that gets a screening row, match or no match, plus a $0.007 start fee per call (one run); nothing is charged per name if no list could be loaded, and error rows are free. 200 names in one call ~ $0.61. Lists load in ~2-5 s from a cache in your Apify account (at most 6 h old) or ~10-40 s when re-downloaded, then hundreds of names/s. RETURNS: {count, items}. type="screening" row per name: query, status (potential_match | no_match), best_score 0-100, match_count, matches[] (list, entry id, primary name, aliases, programs, countries, DOB, score, country_match, dob_match), lists_screened, lists_unavailable, min_score, screened_at, disclaimer; or type="error" rows (code, message). EXAMPLE: {"queries":[{"name":"Banco Nacional de Cuba","entityType":"entity"}],"minScore":90} -> one screening row, status potential_match with OFAC_SDN matches. PARAMETERS: names and/or queries: at least one is required although the schema marks both optional (neither -> a free INPUT_EMPTY error row); both may be combined and every entry is billed once ($0.003), so send all names in ONE call (one $0.007 start fee and one list load per run). Use names for bare strings and queries when you have per-name country / dateOfBirth / entityType or want your own id echoed. Per query, dateOfBirth drops listed individuals whose recorded birth year differs by more than 1 year (entries without a DOB are kept); country only sets country_match (true/false/null) unless excludeNonMatchingCountry=true, which then drops matches whose listed countries exclude it (entries without country data are kept). entityType: a query's own value wins over the top-level one; "entity" means anything that is not an individual (organisations, vessels, aircraft), and list entries of unknown type always pass. lists: omit for all five; a list whose download fails is reported in lists_unavailable rather than failing the run. minScore: a candidate is returned only if its 0-100 name similarity is >= minScore; it does not change the price - 85 default, 90-95 for large batches with fewer false positives, 75-80 for transliterated names. maxMatchesPerName trims matches[] (best first) but match_count still counts all. Not a certified screening service. Automated fuzzy matching against public lists downloaded at run time; results can contain false positives and miss matches. Review every potential match manually and consult official sources and legal counsel before acting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
listsNoOptional, default all five. Any subset of "OFAC_SDN" | "OFAC_CONS" | "EU" | "UK" | "UN" (ids from list_sources). Example: ["OFAC_SDN","EU"]
namesNoOptional if queries is given (at least one of the two is required). Plain person or organisation names (non-empty strings, any order/case/diacritics); $0.003 each. Example: ["Vladimir Putin","Acme Trading LLC"]
queriesNoOptional if names is given. {name, id?, country?, dateOfBirth?, entityType?} objects with per-name filters; $0.003 each. Example: [{"name":"Ali Khamenei","id":"cust-42","country":"IR","dateOfBirth":"1939","entityType":"individual"}]
minScoreNoOptional integer 60-100, default 85. Minimum 0-100 fuzzy name-similarity score for a match. Use 90+ for fewer false positives, ~80 for more recall. Example: 90
entityTypeNoOptional, one of "any" | "individual" | "entity", default "any". Entity filter for all names; a query's own entityType wins. Example: "entity"
maxMatchesPerNameNoOptional integer 1-100, default 10. Top matches returned per name (best first); match_count still reports the total. Example: 3
excludeNonMatchingCountryNoOptional boolean, default false. true removes matches whose listed countries do not include the query country (entries without country data are kept). No effect without queries[].country.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesNumber of rows in items
itemsYesDataset rows: type=screening or type=error

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed13 schema fields changedv1.0.2
    • addedInput schema / properties / entityType / description
      Added value: +"Optional, one of \"any\" | \"individual\" | \"entity\", default \"any\". Entity filter for all names; a query's own entityType wins. Example: \"entity\""
    • addedInput schema / properties / excludeNonMatchingCountry / description
      Added value: +"Optional boolean, default false. true removes matches whose listed countries do not include the query country (entries without country data are kept). No effect without queries[].country."
    • changedInput schema / properties / lists / description
      Previous value: -"Default: all five"New value: +"Optional, default all five. Any subset of \"OFAC_SDN\" | \"OFAC_CONS\" | \"EU\" | \"UK\" | \"UN\" (ids from list_sources). Example: [\"OFAC_SDN\",\"EU\"]"
    • addedInput schema / properties / maxMatchesPerName / description
      Added value: +"Optional integer 1-100, default 10. Top matches returned per name (best first); match_count still reports the total. Example: 3"
    • changedInput schema / properties / minScore / description
      Previous value: -"Default 85"New value: +"Optional integer 60-100, default 85. Minimum 0-100 fuzzy name-similarity score for a match. Use 90+ for fewer false positives, ~80 for more recall. Example: 90"
    • changedInput schema / properties / names / description
      Previous value: -"Names to screen"New value: +"Optional if queries is given (at least one of the two is required). Plain person or organisation names (non-empty strings, any order/case/diacritics); $0.003 each. Example: [\"Vladimir Putin\",\"Acme Trading LLC\"]"
    • changedInput schema / properties / queries / description
      Previous value: -"Names with optional country (ISO-2 or name), dateOfBirth (YYYY or YYYY-MM-DD), entityType"New value: +"Optional if names is given. {name, id?, country?, dateOfBirth?, entityType?} objects with per-name filters; $0.003 each. Example: [{\"name\":\"Ali Khamenei\",\"id\":\"cust-42\",\"country\":\"IR\",\"dateOfBirth\":\"1939\",\"entityType\":\"individual\"}]"
    • addedInput schema / properties / queries / items / properties / country / description
      Added value: +"Optional. ISO 3166-1 alpha-2 code (\"IR\") or English country name (\"Iran\"); adds country_match to each match (filters only with excludeNonMatchingCountry=true)"
    • addedInput schema / properties / queries / items / properties / dateOfBirth / description
      Added value: +"Optional. Format YYYY or YYYY-MM-DD, e.g. \"1939\" or \"1939-04-19\"; drops listed individuals whose birth year differs by more than 1 year"
    • addedInput schema / properties / queries / items / properties / entityType / description
      Added value: +"Optional, one of \"individual\" | \"entity\" | \"any\"; default = top-level entityType. \"entity\" = organisations, vessels, aircraft"
    • addedInput schema / properties / queries / items / properties / id / description
      Added value: +"Optional. Your own reference echoed in the output row, e.g. \"cust-42\""
    • addedInput schema / properties / queries / items / properties / name / description
      Added value: +"Required. Person or organisation name to screen (non-empty), e.g. \"Ali Khamenei\""
    • addedOutput schema / properties / count / description
      Added value: +"Number of rows in items"
  2. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations declare readOnly/idempotent/openWorld, and the description adds substantial behavior beyond them: 6-hour cache vs re-download latency, the Apify Actor dependency and token requirement, the exact pay-per-name/start-fee model with per-name free cases, and failure handling (failed lists surface in lists_unavailable rather than aborting).

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 WHAT/WHEN/COST/RETURNS/PARAMETERS headers, making a long description navigable, and each section earns its place. There is minor redundancy (the 'not a certified screening service' caveat appears twice) that keeps it just short of a 5.

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 paid, open-world, network-dependent tool with 7 optional params, the definition covers cost, return shape, latency, failure modes, and legal caveats; the separate output schema handles the row fields, so nothing an agent needs to invoke it correctly is missing.

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?

Schema coverage is 100%, but the description adds real meaning the schema lacks: precedence rules (a query's entityType beats top-level; unknown-type entries always pass), the DOB ±1-year drop behavior with entries lacking DOB kept, country only setting country_match unless excludeNonMatchingCountry, and the fact that names/queries are billed identically so all names should go in one call.

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?

Opens with a specific verb+resource ('Fuzzy-match people or organisation names') and names the exact scope (OFAC SDN, OFAC Consolidated, EU, UK, UN). It also distinguishes itself from its only sibling by naming list_sources as the tool for coverage questions.

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 WHEN TO USE (KYC/KYB onboarding, vendor checks, bulk re-screening), WHEN NOT TO USE (pointing to list_sources, and disclaiming PEP/adverse-media/certified screening), and an operational directive to batch all names into one call to avoid repeated list loads.

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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