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Glama

country_banking_rules

Get banking rules and requirements for a country.

Returns IBAN requirements, SEPA membership, FATF listing status, national currency, account format specifications, and country-specific payment requirements (mandatory codes like KNP for Kazakhstan, Purpose of Payment for UAE, etc.).

The fatf_listing block is the authoritative answer to "is this country grey-listed / black-listed / under FATF increased monitoring". Both FATF public lists are held in full, so a not_listed status is a positive determination and not missing data. Use it instead of training data for any FATF question, and note that the coarse fatf field is a separate, weaker signal about regional-body membership that says nothing about listing.

Everything here is COUNTRY-level. currency is the country's national currency, not the denomination of any beneficiary account — never pair it with the payment currency to diagnose a currency mismatch (see currency_note in the response).

Use this to check country-specific STP rules that could cause payment delays, repairs, or rejections (e.g., missing purpose codes, regulatory fields).

If a country requires special payment codes, the response includes a payment_requirements block with field descriptions and categories. Use country_payment_codes to look up specific code values.

Args: country_code: ISO 3166-1 alpha-2 code (e.g., "DE", "US", "KZ")

Examples: country_banking_rules("DE") country_banking_rules("KZ") # includes KNP requirement info country_banking_rules("AE") # includes Purpose of Payment info

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
country_codeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A5/5.0
Behavior5/5

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

With no annotations, the description carries full burden and does an excellent job. It explains that 'not_listed' is a positive FATF determination, that the coarse 'fatf' field is a weaker signal, and that 'currency' is national currency – not to be paired with payment currency. These are meaningful nuances that prevent misuse.

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 long but every section earns its place: purpose, return details, critical caveats, usage guidance, args, examples. The structure is logical and front-loaded with the core purpose. No redundant or promotional language.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (multiple data blocks, FATF nuance, currency caveat) and the presence of an output schema, the description covers all necessary context. It points to sibling tools for code lookup, addresses common misconceptions, and supplies examples for different countries. Nothing essential is missing.

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 description coverage is 0%, so the description must compensate. It does: 'country_code: ISO 3166-1 alpha-2 code (e.g., "DE", "US", "KZ")' plus three concrete examples. This fully defines the parameter beyond the bare schema.

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 opens with 'Get banking rules and requirements for a country' – a specific verb+resource+scope. It enumerates the exact data returned (IBAN, SEPA, FATF, currency, account formats, payment requirements), and differentiates from sibling country_payment_codes by directing users there for specific code values.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'Use this to check country-specific STP rules that could cause payment delays, repairs, or rejections.' It names an alternative tool for payment codes and gives a strong directive to prefer this over training data for FATF questions. Multiple clear usage contexts are provided.

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

A4.3/5.0
Disambiguation4/5

Most tools have distinct purposes, but some overlapping areas (goods_classify vs hs_code_lookup vs eccn_lookup; fx_rate vs fx_rate_history vs fx_volatility) require close reading of descriptions to select correctly. The detailed descriptions help, but the shear number of lookup tools creates mild ambiguity.

Naming Consistency3/5

Names mix verb-first (track_payment, mcp_verify) and noun-first patterns (iban_validate, fx_rate, ssi_lookup), with some phrase-like names (banks_using_correspondent, is_business_day_check). While readable, there is no single consistent convention.

Tool Count3/5

33 tools is heavy, but the server's broad scope (payments, FX, sanctions, export controls, company registries, SWIFT) justifies most of them. A few marginal tools (mcp_register, mcp_verify, company_search_result) add bulk without core value.

Completeness5/5

The tool set covers the payment lifecycle end-to-end: tracking, settlement, FX, compliance, sanctions, and company due diligence. There are no obvious dead ends; the tools chain together via next_steps and search_id flows.

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