Search the OpenSanctions database by name or keyword. Returns matching entities ordered by relevance.

Use this for exploratory queries: "find entities named Goldman", "search for companies in Russia". For formal sanctions screening with confidence scores, use match_entity instead.

Results include entity type, datasets (which sanctions lists), and properties. For numeric match scores, use match_entity.

Screen a person or company against sanctions and PEP (Politically Exposed Person) lists using structured properties.

This is the primary screening tool. Provide a schema type and properties for precise matching. The matching algorithm uses name comparison, birth dates, nationalities, and identifiers for scoring.

Scores: 0.0-1.0. Above 0.9 = very high confidence match. 0.7-0.9 = likely match, investigate further. Below 0.7 = possible but uncertain.

PEP = Politically Exposed Person (senior government officials, their families, close associates). PEP status appears in the "topics" property as "role.pep".

Fetch complete details for a specific entity by ID, including all properties, dataset memberships, and relationships to other entities.

Use this after finding an entity via search or match to get the full picture: aliases, addresses, birth dates, related companies, family members, associates.

Relationships come from the OpenSanctions knowledge graph. Types include: ownershipOwner, familyRelative, associate, directorshipDirector, and more.

List available sanctions and PEP datasets in OpenSanctions. No API key required.

Returns dataset names, titles, summaries, and entity counts. Use the optional query parameter to filter. Examples of datasets: "us_ofac_sdn" (US OFAC), "eu_fsf" (EU Financial Sanctions), "un_sc_sanctions" (UN Security Council), "gb_hmt_sanctions" (UK HMT).

Get full details about a specific dataset: description, publisher, entity count, last updated, and coverage information. No API key required.

Use list_datasets first to find dataset names, then this tool for details.

Run a multi-step compliance investigation on a person or company. This is the most powerful tool — it combines matching, entity details, and relationship traversal in one call.

Steps: (1) Match the name against sanctions/PEP lists, (2) Fetch full details and relationships for top matches, (3) Return structured data with scores, datasets, and connected entities.

Returns data only — no risk judgments. You (the AI) should interpret the scores, dataset memberships, and relationships to provide context to the user.

Requires both name and schema (Person/Company). Provide additional properties like birthDate or nationality for better match precision.