banking-intelligence

federal_register_changes

Get recent US regulatory changes from BIS and OFAC.

Returns Federal Register publications including entity list updates, rule changes, country policy shifts, and new sanctions programs.

Args: agency: Filter by agency — "BIS" (Bureau of Industry and Security) or "OFAC" (Office of Foreign Assets Control). Omit for both. category: Filter by change category — "entity_list", "rule_change", "country_policy", or "sanctions". Omit for all categories. severity: Filter by severity — "critical", "high", "medium", or "low". Omit for all severity levels. days: Number of days to look back (1–365). Default: 30. limit: Maximum number of results to return. Default: 50.

Examples: federal_register_changes() # Last 30 days, all federal_register_changes(agency="OFAC", days=7) # OFAC changes this week federal_register_changes(category="entity_list", severity="critical")

Use case: 'Any new entity list additions affecting China?'

Input Schema

TableJSON Schema

Name	Required	Description	Default
`days`	No
`limit`	No
`agency`	No
`category`	No
`severity`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.7/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden. It clearly indicates the tool is read-only ('Returns Federal Register publications'), and describes the scope (date range, filters). It could explicitly state it does not modify data, but the context is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: a concise one-line purpose, a brief summary of return contents, then a clear 'Args' list. While the Args section is detailed, it is necessary given the parameter count. The examples and use case add value without redundancy.

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 tool has an output schema (so return format is covered elsewhere), the description covers all input parameters with examples and a use case. It provides sufficient context for an agent to select this tool over siblings and invoke it correctly.

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?

Despite 0% schema description coverage, the description thoroughly explains all five parameters—their purpose, accepted values (e.g., 'BIS', 'OFAC', 'entity_list'), defaults, and constraints (days 1–365). This fully compensates for the missing schema descriptions.

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 clearly states the tool retrieves 'recent US regulatory changes from BIS and OFAC' and lists specific categories like entity list updates, rule changes, etc. This specific verb+resource combination distinguishes it from sibling tools like 'country_export_controls' and 'sanctions_screen'.

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?

The description provides multiple examples and a concrete use case ('Any new entity list additions affecting China?'), explicitly showing when and how to use the tool. The parameter descriptions and default values further guide appropriate usage.

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.

Resources

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