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search_red_flags

Search AML red flags by describing your scenario in natural language and applying filters like product, industry, geography, and more.

Instructions

Search AML red flags using natural-language context and optional filters.

Successful responses include table-ready display hints in display and a portable Markdown fallback in markdown_table; clients decide how to render them.

Agent guidance: use classify_red_flag_request before searching for ambiguous "what red flags apply" requests; skip that extra call when the user already gives specific metadata filters or a concrete scenario. If the user's request is vague, briefly ask for product/channel, industry, customer profile, geography, and transaction channel or volume before searching. If the request already names those details or has a specific scenario, search directly. Call list_filters when you need valid filter values. Use filter_red_flags for exact metadata requests and exhaustive enumeration; use search_red_flags for ranked relevance questions and increase limit for more ranked results because search has no cursor. For broad investigative topics such as human trafficking red flags, use subjects as an eligibility filter. Category is the primary record classification; subjects is a broader eligibility layer that catches cross-category flags; typology_family is a broader proceeds or typology grouping. For example, a human-trafficking-relevant darknet crypto flag can have category="virtual_currency" while matching subjects=["human_trafficking"]. For broad sector requests such as trade logistics red flags, use industry_groups as an eligibility filter. regulator_jurisdiction describes issuer jurisdiction; geographic_footprints describes affected geography or typology geography. For country or jurisdiction requests about issuing regulators, translate names to regulator_jurisdiction codes before filtering, such as France -> FR, Singapore -> SG, Australia -> AU, United Kingdom/UK -> GB, United States/US -> US, and European Union/EU regulators -> EU.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
categoryNo
subjectsNo
risk_levelNo
product_typesNo
industry_typesNo
industry_groupsNo
customer_profilesNo
geographic_footprintsNo
regulator_jurisdictionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations provided, so description fully carries the burden. Discloses response format (display hints, markdown table), lack of cursor (increase limit for more results), and explains semantics of key filters like category, subjects, typology_family, regulator_jurisdiction, geographic_footprints.

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?

Relatively long (~300 words) but well-structured with clear sections: purpose, response format, agent guidance. Each sentence adds value; no fluff. Slightly verbose but efficient.

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?

Thorough guidance for a tool with 11 parameters, no schema descriptions, and 7 siblings. Covers usage scenarios, parameter semantics, response details, and even country code mappings. Fully compensates for missing schema descriptions.

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 description coverage is 0%, but description adds significant semantics: explains difference between category, subjects, and typology_family; clarifies regulator_jurisdiction vs geographic_footprints; gives examples of country code translations. Does not cover every parameter individually but compensates well.

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?

Clearly states the tool searches AML red flags using natural-language context and optional filters. Differentiates from siblings like classify_red_flag_request, filter_red_flags, and list_filters by specifying distinct use cases.

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?

Provides explicit guidance on when to use this tool vs alternatives: use classify_red_flag_request for ambiguous requests, skip for specific metadata filters; use filter_red_flags for exact metadata; use list_filters for valid values. Also advises on handling vague requests.

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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