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AnChainAI
by AnChainAI

search_global_sanctions

Search global sanctions databases with exact field matching across multiple jurisdictions to identify individuals, entities, vessels, aircraft, and cryptocurrency wallets subject to sanctions.

Instructions

Search international sanctions databases using exact field matching across multiple jurisdictions.

Args: dataset: Global sanctions dataset to search in (global, au, ca, ch, eu, gb, il, jp, un, za, zm). Default: global type: Entity type (individual, entity, vessel, aircraft) name: Entity name address: Physical address country: Country or nationality birth_date: Date of birth legal_form: Legal entity type registration_number: Registration number incorporation_date: Incorporation date jurisdiction: Incorporation jurisdiction wallet: Cryptocurrency wallet addresses

Cost: 20 credits

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
typeNo
walletNo
addressNo
countryNo
datasetNoglobal
birth_dateNo
legal_formNo
jurisdictionNo
incorporation_dateNo
registration_numberNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description carries the burden. It discloses that matching is exact, covers multiple jurisdictions, and costs 20 credits. However, it does not explain behavior when no parameters are provided, response format, or potential pagination/limits. Some transparency is provided but gaps remain.

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 opens with a clear one-sentence purpose, then efficiently lists parameters with single-line explanations, followed by cost. No redundant text; every sentence earns its place.

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?

Given the tool's moderate complexity, the description covers purpose, parameters, datasets, and cost. Since an output schema exists, return value details need not be explained. Minor missing context includes match behavior across multiple fields and any filter combinations, but overall it is sufficiently complete for selection and invocation.

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 0%, yet the description compensates fully by listing all 11 parameters with concise meanings, including allowed values for dataset and type, and the default for dataset. This provides clear semantic meaning that the schema itself lacks.

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 clearly states the tool searches international sanctions databases with exact field matching across multiple jurisdictions. This distinguishes it from sibling tools like fuzzy_search_global_sanctions and screen_global_sanctions_address by emphasizing exact matching and broad coverage.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for exact-match sanctions searching and lists available datasets, but does not explicitly state when to prefer this tool over fuzzy search or other screening tools. No exclusions or alternatives are mentioned, so guidance is only implicit.

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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