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Prax Data Services

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Sanctions screening

screen_entity
Read-onlyIdempotent

Sanctions screening for people, companies, vessels and crypto addresses: fuzzy name match (aliases, transliterations) or exact wallet / contract lookup against the sanctions lists Prax pulls continuously: US OFAC (SDN and Consolidated), UK OFSI, EU, UN and Swiss SECO, every crypto address they list on any chain. Returns match / possible_match / no_match with hits (entity, type, programs, list, aliases, linked crypto addresses, score). Use for KYC / KYB onboarding and payment screening. Free plan.

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": {
      "name": "<name>"
    },
    "name": "screen_entity"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYes
typeNooptional: individual, entity, vessel, aircraft
limitNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds genuinely new behavioral context: the specific lists queried (OFAC SDN/Consolidated, OFSI, EU, UN, SECO, plus crypto addresses on any chain), the match/possible_match/no_match outcome space with hit fields, and fuzzy-match treatment of aliases and transliterations.

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 core purpose, matching modes, covered lists and return values are front-loaded and information-dense. It is somewhat run-on with a long colon-delimited list, but nearly every clause adds a fact the agent needs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description correctly explains the return shape (match/possible_match/no_match plus hit fields). It covers data sources, matching modes and use case well; the only notable gap is the behavior of `limit` and any pagination/capping semantics.

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

Parameters3/5

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

Schema coverage is only 33%, so the description carries part of the burden. It usefully clarifies that `name` accepts a person, company, vessel or crypto address/contract, and the `type` values are documented in the schema. However `limit` is never explained in either place, leaving one of three parameters opaque.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: sanctions screening for people, companies, vessels and crypto addresses, including both fuzzy name match and exact wallet/contract lookup. The scope is concrete and enumerates the covered lists. It does not explicitly distinguish itself from the sibling screen_wallet, so it falls short of a 5.

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?

"Use for KYC / KYB onboarding and payment screening" gives a clear use context, and the description distinguishes fuzzy-name versus exact-address modes. It stops short of stating when to prefer a sibling tool or when this tool is not appropriate.

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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