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Glama

AgentEconomy Compliance API

aml_screen

Destructive

Screen an entity against Anti-Money Laundering (AML) watchlists including OFAC SDN and UN sanctions. Returns risk level, risk score, recommendation (approve/review/reject), and sanctions match details. Uses Jaro-Winkler fuzzy name matching for accurate detection. Cost: $0.05 USDC per screening. Caller must supply a signed X-PAYMENT header on the MCP HTTP request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
amountNoTransaction amount
currencyNoCurrency code (e.g. USDC, ETH, BTC)
entity_nameYesFull legal name of the person or entity to screen
transaction_idNoTransaction identifier for audit trail
wallet_addressNoBlockchain wallet address to check

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

The description adds meaningful behavioral context beyond annotations: it mentions the Jaro-Winkler fuzzy matching algorithm, the exact cost, and the X-PAYMENT authentication requirement. It also states the return fields. It does not elaborate on the destructiveHint, leaving that annotation unexplained, but the side-effecting payment behavior makes the annotations non-contradictory.

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 four sentences, front-loaded with purpose, then returns, algorithm, cost, and authentication. Every sentence adds critical information with no fluff or repetition of the schema.

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 absence of an output schema, the description adequately lists the key return fields, and it covers prerequisites (payment header) and cost. It does not address error handling or failover scenarios, but for a screening tool this is reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the parameter descriptions already document every field. The description adds modest value by indicating that fuzzy matching applies to entity names and that the tool returns sanctions match details, but it does not enrich parameter semantics meaningfully.

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 states a specific action ('Screen an entity against AML watchlists'), names concrete resources (OFAC SDN, UN sanctions), and lists expected return values (risk level, risk score, recommendation, sanctions match details). This clearly differentiates from sibling tools like kyc_check or risk_report by focusing on AML-specific watchlists and output.

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 clearly communicates when to use the tool: when AML watchlist screening is needed. It also provides necessary usage prerequisites such as the signed X-PAYMENT header and cost per screening. However, it does not explicitly contrast with sibling tools or state exclusions, so it stops short of full alternative guidance.

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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TDQS

A3.9/5.0
Disambiguation4/5

The tools are mostly distinct: aml_screen targets entity screening with fuzzy matching, kyc_check focuses on identity verification with PEP data, risk_report aggregates both, and wallet_screen handles blockchain addresses. However, aml_screen and kyc_check both involve sanctions list checks and have overlapping outputs (risk score, sanctions matches), which could cause initial selection confusion.

Naming Consistency3/5

Tool names mix verb styles: 'aml_screen' and 'wallet_screen' use the verb 'screen', while 'kyc_check' uses 'check' and 'risk_report' uses 'report'. The pattern is not uniform (e.g., one could expect 'screen_aml' or 'report_risk'), but the names are still readable and indicate their respective functions.

Tool Count5/5

With 4 tools, the server is well-scoped for a compliance API. Each tool covers a distinct aspect (AML screening, KYC checks, holistic risk reports, wallet screening) without unnecessary bloat or missing core functionality.

Completeness4/5

The tool set covers the primary compliance workflows: sanctions screening, identity verification, combined risk assessment, and crypto wallet risk analysis. Minor gaps exist, such as separate PEP-only screening or transaction monitoring, but the core lifecycle for compliance checks is well represented.

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