Screen a name against the five official sanctions lists (EU, US OFAC SDN, UN, UK FCDO, French asset freezes) from a snapshot refreshed daily, with per-list dates. Query: name=NAME (optional threshold=0.6-1.0, lists=eu,ofac,un,uk,fr). Returns scored matches with entity type and programs, and a verdict: match, possible_match or no_match. Price: $0.01 USDC per call (x402).
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| lists | No | ||
| threshold | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description carries full burden. It discloses daily snapshot, scoring, verdict types, and pricing. However, it doesn't mention if it's read-only or any side effects, though that's implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single paragraph efficiently covers purpose, query format, return structure, and pricing. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters and no output schema, the description covers input constraints, return values (scored matches, verdict), and pricing. Includes snapshot freshness. Complete for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description fully explains all three parameters: name is required, threshold with range 0.6-1.0 and default 0.85, lists as string of list codes. Adds meaning beyond bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it screens a name against five specific sanctions lists, with daily refresh and per-list dates. It distinguishes from sibling tools which are business validation tools, making its unique purpose explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides query syntax and optional parameters (threshold, lists) but lacks explicit guidance on when not to use this tool or compare to alternatives. The context implies it's for sanctions screening, but no exclusions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool serves a distinct verification or lookup purpose (e.g., company lookup, sanctions screening, address geocoding, IBAN validation) with no overlap. Even tools targeting the same source (e.g., check_eori vs. validate_vat_eu) have clearly different inputs and outputs.
Almost all tools follow a verb_noun pattern (verify, validate, lookup, check, find, screen) with clear nouns. The only exception is 'catalog' (a noun-only name), but it's a minor deviation that doesn't cause confusion.
19 tools is appropriate for a data verification service covering company data, sanctions, VAT, addresses, emails, IBANs, and financials. Each tool addresses a specific need without being excessive or sparse.
The tool set covers the full lifecycle of EU company verification: existence, status, financials, ownership, VAT, sanctions, EORI, invoices, tenders, and address validation. No obvious gaps for its stated domain.