UK Business Tools - Ledgerhall

Screen a Name Against Sanctions Lists

dd_sanctions_screen

Read-onlyIdempotent

Screen a name against the UK (OFSI), US (OFAC), EU and UN consolidated sanctions lists.

Returns every list entry whose primary name or alias matches, with the regime, source reference and listing date. Use it to check whether a counterparty — or its officers / persons with significant control — appears on a sanctions list.

MATCHING is deterministic: normalised exact + alias match (case-, accent- and punctuation-insensitive). A company/entity legal name matches reliably; PERSON names with transliteration variants may not (e.g. 'Mohammed' vs 'Muhamad'). An empty result is therefore NOT a guarantee of clearance, and a hit on a common name may be a false positive to disambiguate. This is a screening aid, not a compliance determination.

lists_screened reports which of OFSI/OFAC/EU/UN were actually loaded — if any is missing the result is partial. as_at is when the lists were last refreshed on this server.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes	Person or company/entity name to screen against the consolidated sanctions lists.
`entity_type`	No	Optional filter: 'person' or 'entity'. Omit to screen both.

Output Schema

TableJSON Schema

Name	Required	Description
`hits`	No	Matching list entries. An empty list means no exact/alias match on the screened lists — NOT a guarantee of clearance (see the tool description on matching limits).
`as_at`	No	When this server last refreshed the loaded lists (ISO timestamp). Provenance for the screen — the lists update on designation.
`query`	Yes	The name that was screened.
`match_count`	Yes	Number of list entries that matched the query.
`lists_screened`	No	Which consolidated lists were loaded and actually screened for this call. A list absent here failed to load and was NOT screened — treat the result as partial if any of OFSI/OFAC/EU/UN is missing.
`normalized_query`	Yes	The normalised form used for matching (upper-cased, accent- and punctuation-stripped, whitespace-collapsed).
`entity_type_filter`	No	entity_type filter applied to the screen ('person'/'entity'), or null.

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description goes beyond by explaining matching normalization, false positive/negative risks, partial results when lists are missing, and the meaning of lists_screened and as_at. This adds substantive behavioral context that structured annotations alone do not convey, with no contradictions.

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 structured into focused paragraphs: purpose, matching semantics and caveats, and output field explanations. Every sentence provides essential information for a compliance screening tool. It is front-loaded with the main purpose and does not waste words on repetition.

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?

The tool is complex due to compliance implications, but the description thoroughly covers return values (regime, source reference, listing date), data freshness, and partial-result indicators. Even though an output schema exists, the description's explication of lists_screened and as_at is valuable. Combined with the detailed matching caveats, the description is complete for an agent to invoke and interpret results correctly.

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 100%, so the baseline is 3. The description adds extra meaning for the 'name' parameter by explaining normalization (case/accent/punctuation-insensitive) and transliteration pitfalls, which helps users interpret results. It does not add much for 'entity_type', but the existing schema description suffices. Overall, the added matching semantics justify a 4.

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 screens names against UK OFSI, US OFAC, EU, and UN consolidated sanctions lists. This specific verb+resource+scope distinguishes it from all sibling tools, which are general search/profile tools. No ambiguity about what the tool does.

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 explicitly says to use it to check whether a counterparty or associated persons appear on sanctions lists. It also provides critical usage caveats: matching is deterministic, person-name transliteration variants may not match, empty results are not a guarantee, and hits may be false positives. This gives clear when-to-use and when-to-be-cautious guidance, with no competing alternatives in the sibling list.

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

A4/5.0

Disambiguation4/5

Tools are well-grouped by domain prefixes (dd_, gov_, law_, prop_) with clear descriptions that differentiate them. However, there is minor overlap, e.g., dd_search could be used instead of individual searches, and dd_fetch versus dedicated profile tools might cause confusion.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with domain-specific prefixes (dd_, gov_, law_, prop_). Names are descriptive and predictable, e.g., dd_charity_search, gov_govuk_search, law_bills_search_bills.

Tool Count4/5

70 tools is high but justified by the broad scope covering due diligence, government, legal, and property domains. Each domain has a reasonable number of tools (about 15-20 each). The count is on the upper end but still manageable.

Completeness5/5

The tool set is comprehensive across all domains: full CRUD for companies and charities, detailed legal research (cases, legislation, parliament, citations), property data (EPC, planning, price paid, rentals), and government information. No obvious gaps for the intended use cases.

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