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sanctions_search_alias

Read-onlyIdempotent

Search aliases / AKAs across selected lists. Distinct from screen_entity in that only the alias fields are matched, which is helpful when the primary listed name differs sharply from the popular spelling.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum matches to return. Defaults vary per tool.
queryYesAlias / AKA to search for.
sourcesNoRestrict screening to a subset of source lists. Defaults to all four. Allowed: OFAC_SDN, EU_CFSP, UN_SC, BIS_DPL.
thresholdNoMinimum confidence score (0..1) for a result to be returned. Defaults to 0.85.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changed
    • addedInput schema / properties / limit / maximum
      Added value: +100
    • addedInput schema / properties / limit / minimum
      Added value: +1
    • changedInput schema / properties / limit / type
      Previous value: -"number"New value: +"integer"
    • addedInput schema / properties / threshold / maximum
      Added value: +1
    • addedInput schema / properties / threshold / minimum
      Added value: +0
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds a genuine behavioral trait beyond that: matching is restricted to alias/AKA fields only, not primary names. It doesn't discuss result format, but with annotations carrying the safety burden this is solid.

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?

Two tight sentences, front-loaded with the core action and immediately followed by the differentiating scope. Every clause earns its place with no filler.

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?

For a read-only search with full schema coverage and no output schema, the description supplies the two things an agent most needs – the resource matched (aliases/AKAs) and how it differs from the entity screen. It stops short of noting how to interpret confidence/threshold results, a minor gap.

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 description coverage is 100%, so the schema already documents query, sources, threshold and limit with defaults and ranges. The description adds no additional syntax, format, or fuzzy-matching detail about the query parameter, so the baseline 3 applies.

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?

States a specific verb+resource ('Search aliases / AKAs across selected lists') and explicitly distinguishes itself from a sibling ('Distinct from screen_entity in that only the alias fields are matched'). An agent can tell it apart from sanctions_screen_entity without opening either schema.

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?

Provides a clear use condition ('helpful when the primary listed name differs sharply from the popular spelling') and contrasts scope with the sibling screen_entity. It lacks an explicit when-not-to-use, but the context is strong enough for correct selection.

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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