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Screen a name against sanctions lists

screen_sanctions

Screens a person or company name against official sanctions lists: OFAC SDN and OFAC Consolidated (US Treasury), the UN Security Council Consolidated List, the EU consolidated financial sanctions list and the UK Sanctions List, primary names and aliases. Matching ignores case, accents, punctuation, word order and legal forms (LLC, PJSC, GmbH), and tolerates one typo in a word of 4+ letters and common transliterations (Sergei, Sergey, Sergej). Each match has a score from 0 to 1 and a match_type (exact, alias, fuzzy); only scores at or above min_score (default 0.85) are returned, at most 10 designated parties. Always reports which lists were screened, which were unavailable and when each was last refreshed: keep 'data_as_of' with the result as evidence of the check. Latin-script names only; dates of birth, nationalities and addresses are not compared. A match is a signal for enhanced due diligence, never a legal determination. Costs 1 credit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPerson or company name to screen, in Latin script, e.g. 'Rosneft Oil Company PJSC' or 'Sergei Aksyonov'
min_scoreNoOptional. Lowest match score returned, 0.7 to 1, default 0.85. Lower finds more spelling variants and more false positives, higher fewer of both, e.g. 0.9

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / min_score
      Added value: +{
+  "description": "Optional. Lowest match score returned, 0.7 to 1, default 0.85. Lower finds more spelling variants and more false positives, higher fewer of both, e.g. 0.9",
+  "maximum": 1,
+  "minimum": 0.7,
+  "type": "number"
+}
    • changedInput schema / properties / name / description
      Previous value: -"Person or company name to screen, e.g. 'Acme Trading LLC'"New value: +"Person or company name to screen, in Latin script, e.g. 'Rosneft Oil Company PJSC' or 'Sergei Aksyonov'"
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so: matching normalization rules (case, accents, punctuation, word order, legal forms), fuzzy tolerance (one typo in 4+ letter words, transliterations), score range and type, the 10-party cap, list-availability reporting, Latin-script-only limits, the exclusion of DOB/nationality/address comparison, and a 1-credit cost. This is unusually thorough behavioral disclosure.

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?

A single dense paragraph whose sentences each carry distinct payload (what/list scope, matching behavior, scoring, output/evidence, limitations, cost). It is front-loaded with the core purpose, though the length pushes the practical guidance (cost, limitations) toward the end.

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?

There is no output schema and no annotations, yet the description explains the returned fields (score, match_type, data_as_of, lists screened/unavailable/last refreshed), the cap of 10 results, and the non-legal nature of matches. An agent has everything needed to invoke and interpret this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds genuine meaning: it explains the trade-off behind min_score (lower finds more variants and more false positives, higher finds fewer of both) and restates the default 0.85 and the Latin-script constraint on the name. That is more than the schema alone conveys.

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?

States a specific verb (screens) and resource (a person or company name) and enumerates the exact lists consulted: OFAC SDN, OFAC Consolidated, UN, EU, UK. This is immediately distinguishable from the sibling tools (verify_company, verify_vat, validate_iban, enrich_company), none of which perform sanctions screening.

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?

Gives clear usage context: 'A match is a signal for enhanced due diligence, never a legal determination' and instructs the agent to retain 'data_as_of' as evidence of the check. It does not explicitly state when to prefer this over a sibling or when not to call it, but no sibling offers overlapping functionality, so the routing gap is minor.

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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