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Hei33enberg

WhiteIntel MCP Server

by Hei33enberg

get_sanctions

Screen an entity for direct and cluster-linked sanctions or crime exposure, returning source URLs for each signal.

Instructions

Return an entity's screening exposure for the entity AND its resolved cluster siblings, each with a source URL. IT IS NOT SANCTIONS-ONLY, DESPITE THE NAME — read each row's signal_type. Measured 2026-08-11: BARCLAYS BANK PLC came back sanctioned: false with one signal of signal_type: 'crime' (severity HIGH, source_list opensanctions_crime, a criminal/wanted listing reaching it via its cluster). Only signal_type: 'sanctioned' rows are sanctions designations, and only those reliably carry list and regime — on the crime row both were null, so do not read a null list as missing data. Two consequences: a sanctioned: false response can still contain a HIGH-severity adverse finding you must report, and 'no sanctions signal' (what the top-level flag and note describe) is not 'nothing found'. Response splits the top-level flag: sanctioned_self = a direct listing ON this entity; sanctioned_via_cluster = the flag reaches it ONLY via a cross-source cluster sibling (~2.3% false-positive tail on UK OpenOwnership resolution — treat cluster-only hits as a lead until you verify the sibling really is the same real-world party). The aggregate sanctioned (self OR cluster) is preserved for back-compat. Get the id from search_entities or lookup_by_identifier.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesEntity id.
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it does so thoroughly. It reveals cluster expansion, signal_type semantics, null list/regime behavior, the distinction between sanctioned_self and sanctioned_via_cluster, the false-positive tail, and back-compat behavior. This is far beyond what annotations would have provided.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every sentence earns its place: it front-loads the core function, then delivers critical caveats, a concrete measured example, flag semantics, and id-source guidance. The structure is logical and dense without redundancy, which is appropriate given the tool's misleading name and nuanced behavior.

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?

Given the tool's complexity, the absence of an output schema, and the absence of annotations, the description is remarkably complete. It covers return contents, signal types, null handling, flag meanings, false-positive risk, and how to obtain the required id. An agent has enough information to invoke the tool and interpret its results 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?

The input schema already fully documents the single `id` parameter with 100% coverage. The description adds useful semantic value by specifying that the id should come from search_entities or lookup_by_identifier, and by framing the id as an entity id. This goes beyond the schema's bare 'Entity id' description.

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: 'Return an entity's screening exposure for the entity AND its resolved cluster siblings, each with a source URL.' It also explicitly distinguishes the tool from its misleading name by clarifying it is not sanctions-only and by directing attention to `signal_type`. This clearly separates it from sibling tools like get_entity or lookup_by_identifier.

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 provides strong contextual guidance: it explains what the tool returns, warns that a `sanctioned: false` response can still contain adverse findings, and tells the agent to get the id from search_entities or lookup_by_identifier. It does not explicitly enumerate when not to use the tool versus alternatives, but the context is clear enough for an agent to select it appropriately.

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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