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diplv

companylens-mcp

Sanctions Screening

company_sanctions_check

Check companies against global sanctions lists (OFAC, EU, UN) to identify potential compliance risks using automated screening with confidence scores.

Instructions

Screen a company against global sanctions lists (OFAC, EU, UN) via OpenSanctions. Returns match confidence scores. Use company_search first to get an entity_id. IMPORTANT: This is an automated screening, not a legal determination.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_idYesCompanyLens entity ID from company_search (starts with "companylens_")

Implementation Reference

  • The handler function for company_sanctions_check that fetches sanctions data via apiCall and formats the output.
    async ({ entity_id }) => {
      const data = await apiCall(`/v1/company/${entity_id}/sanctions`) as {
        name: string;
        is_sanctioned: boolean;
        matches: Array<{ name: string; score: number; datasets: string[] }>;
        agent_hint: string;
      };
    
      let text: string;
      if (data.is_sanctioned) {
        const matchLines = data.matches.map((m) =>
          `- ${m.name} (confidence: ${(m.score * 100).toFixed(0)}%, lists: ${m.datasets.join(', ')})`
        );
        text = `WARNING: ${data.name} has potential sanctions matches:\n\n${matchLines.join('\n')}\n\n${data.agent_hint}`;
      } else {
        text = `${data.name}: No sanctions matches found.\n\n${data.agent_hint}`;
      }
    
      return { content: [{ type: 'text' as const, text }] };
    },
  • The input schema and description for company_sanctions_check.
    {
      title: 'Sanctions Screening',
      description: 'Screen a company against global sanctions lists (OFAC, EU, UN) via OpenSanctions. Returns match confidence scores. Use company_search first to get an entity_id. IMPORTANT: This is an automated screening, not a legal determination.',
      inputSchema: z.object({
        entity_id: z.string().describe('CompanyLens entity ID from company_search (starts with "companylens_")'),
      }),
    },
  • src/index.ts:79-80 (registration)
    Registration of the company_sanctions_check tool.
    server.registerTool(
      'company_sanctions_check',
Behavior4/5

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

No annotations provided, but description carries significant burden well: discloses data source (OpenSanctions), output format ('match confidence scores'), and critical legal disclaimer ('automated screening, not a legal determination'). Missing rate limits or error behaviors, but covers essential risk context for sanctions screening.

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?

Four sentences, zero waste: purpose/source, return value, prerequisite instruction, and legal disclaimer. Front-loaded with specific action, each sentence delivers unique essential information appropriate for a high-stakes compliance tool.

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?

Appropriate for low complexity (1 param). Compensates for missing annotations with legal disclaimer and output description ('match confidence scores'). No output schema exists, but description acknowledges return type. Could mention failure modes (no matches found), but adequate for tool complexity.

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 coverage is 100% (entity_id fully documented), establishing baseline 3. Description adds valuable workflow context indicating the parameter must be obtained from company_search and hints at format ('companylens_'), exceeding baseline by providing usage semantics beyond static schema definitions.

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?

Excellent specificity: verb ('Screen'), resource ('company'), scope ('global sanctions lists OFAC, EU, UN'), and data source ('via OpenSanctions'). Clearly distinguishes from sibling tools by specifying sanctions-specific functionality versus contracts, court cases, or general profile/search.

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?

Strong prerequisite instruction ('Use company_search first to get an entity_id') establishes explicit workflow sequence with sibling tool. Lacks explicit 'when not to use' or alternative recommendations (e.g., when to use profile vs sanctions), but the dependency guidance is clear and actionable.

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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