Ariadne Nexus

Zero-Trust OFAC SDN & Global Sanctions Entity Screening

screen_ofac_entity

Read-onlyIdempotent

Screen a company, vessel, wallet counterparty, or individual against OFAC SDN and consolidated sanctions lists via exact identifier and fuzzy name matching, returning a compliance verdict and audit proof.

Instructions

Screens a corporate entity or individual counterparty against the U.S. Treasury OFAC Specially Designated Nationals (SDN) and consolidated sanctions registries using exact identifier matching (LEI/CIK/CRN) and Jaro-Winkler fuzzy phonetic resolution, returning a compliance verdict and SHA-256 Merkle audit proof. Use when: verifying whether a specific company, vessel, wallet counterparty, or individual is sanctioned before onboarding or settlement. Do not use when: retrieving macro-level weekly sanctions policy trends (use get_sovereign_policy_brief instead) or drafting regulatory comment letters on FinCEN/OFAC rulemakings (use synthesize_regulatory_docket instead). Behavior: Read-only, deterministic compliance check ($0.50 USD per call via apiKey or paymentToken; returns an x402/MPP paymentChallenge without charging if credentials are omitted).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "entity_name": "<entity_name>"
    },
    "name": "screen_ofac_entity"
  }
}

Input Schema

Name	Required	Description	Default
`apiKey`	No	Optional Ariadne Nexus Developer API key ("nexus_live_...") for authenticated execution. May also be passed via the x-api-key HTTP header.
`country`	No	ISO 3166-1 alpha-2 two-letter jurisdiction code of incorporation or residence (e.g., "US", "RU", "IR", "GB"; default: "US").	US
`entity_name`	Yes	Full legal name of the corporation, financial institution, vessel, or individual to screen (e.g., "Vnesheconombank", "Blackstone Inc.").
`paymentToken`	No	Optional Stripe Shared Payment Token ("spt_...") or settled x402 payment receipt hash ("rcpt_...") for pay-per-call execution ($0.50 USD).
`fuzzy_threshold`	No	Jaro-Winkler string similarity threshold between 0.60 (broad alias capture) and 0.95 (strict match) required to flag a match (default: 0.85).
`registration_number`	No	Optional 20-character Legal Entity Identifier (LEI), Corporate Registration Number (CRN), or 10-digit SEC Central Index Key (CIK) for exact registry matching.

Output Schema

Name	Required	Description
`tool`	Yes	Executed tool identifier ("screen_ofac_entity").
`receipt`	No	Cryptographic payment or API key settlement receipt ID.
`verdict`	Yes	Forensic sanctions screening result and cryptographic proof.
`entity_name`	Yes	Screened legal entity name.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.3.3Nov 11, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, but the description goes well beyond them: determinism, a $0.50 per-call cost model via apiKey or paymentToken, and the important edge behavior that an omitted credential returns an x402/MPP paymentChallenge without charging. These are exactly the auth and cost traits annotations cannot express.

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: what it does, then when/when-not, then behavioral/pricing notes. Dense but every clause carries information; the heavy parentheticals and layered 'Behavior:' sentence make it slightly long for an agent scanning several tools, but nothing is wasted.

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 an output schema present, the description need not explain return values, and it still flags the verdict and audit proof. Combined with annotations and full schema coverage, an agent has everything needed to decide, invoke, and pay for the call.

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 description coverage is 100%, so the baseline is 3; the description still adds framing by tying registration_number-style identifiers (LEI/CIK/CRN) to exact registry matching and fuzzy_threshold to Jaro-Winkler phonetic resolution as a combined strategy, which the per-parameter schema text does not state jointly. It does not, however, mention country or the credential parameters.

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 and resource ('Screens a corporate entity or individual counterparty against... SDN and consolidated sanctions registries') and even names the matching mechanisms (exact identifier matching, Jaro-Winkler fuzzy resolution) and the output (compliance verdict + Merkle audit proof). It is unmistakable against siblings like get_sovereign_policy_brief or synthesize_regulatory_docket.

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 'Use when' clause gives the triggering condition (sanctions verification before onboarding or settlement) and the 'Do not use when' clause names two concrete siblings with the scenario that selects each. Nothing is left to inference.

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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