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Sugra-Systems

sugra-api-mcp

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sugra_entity_lookup

Read-onlyIdempotent

Resolve an entity by LEI or VAT identifier to retrieve a KYB envelope including identity, sanctions screening, and optional ownership or adverse media details.

Instructions

Resolve an entity by identifier and return its composed KYB envelope.

anchor is lei (Legal Entity Identifier, resolved via the GLEIF registry) or vat (EU VAT number, validated via the EU VIES service). The result weaves identity, a sanctions screening signal, and - on request - ownership and adverse-media slices.

The screening verdict is a SCREENING SIGNAL, not a compliance determination, and any PEP / adverse-media content is supplementary and non-comprehensive. The disclaimer field carries this and is always present.

Output is COMPACT by default to protect the agent context budget: {entity:{name, anchor, value, status, country}, screening:{status, top_matches:[...3], hit_count}, ids:{...}, disclaimer}. Pass include to opt INTO fuller per-slice detail, e.g. include=["ownership","adverse_media"] adds those slices in full form.

On a bad anchor or an API error this returns a clean {error, detail} dict rather than raising, so the agent can branch on result.get("error").

Args: anchor: Identifier type, one of lei or vat. value: The identifier value (the 20-char LEI code or the VAT number). include: Optional list of fuller slices to add, e.g. ["ownership", "adverse_media"]. Omit for the compact default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYes
anchorYes
includeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

The description adds significant context beyond annotations: it clarifies the screening verdict is a signal, not a compliance determination, and that adverse-media/ownership content is supplementary and non-comprehensive. It details the default compact output and the optional include parameter behavior, plus error handling (returns {error, detail} instead of raising). No contradictions with annotations (readOnlyHint, idempotentHint, etc.).

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 well-structured with clear paragraphs and bullet points, front-loading the main purpose. Every sentence adds value, covering purpose, anchor types, output shape, caveats, and error handling without unnecessary verbosity.

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's complexity (3 parameters, output schema exists), the description is highly complete. It covers all behavioral aspects: compact vs. full output, error handling, warnings about screening signal, and the disclaimer field. No gaps remain.

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?

With 0% schema description coverage, the description fully compensates: it explains anchor (enum of lei/vat), value (format hints for LEI or VAT), and include (optional list enabling fuller slices with examples). It also describes default behavior when include is omitted.

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 states the tool resolves an entity by identifier (LEI or VAT) and returns a composed KYB envelope. It clearly distinguishes the two anchor types and the purpose, making it distinct from siblings like 'sugra_entity_screen' which likely focuses on screening alone.

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?

The description explains when to use each anchor type (lei via GLEIF, vat via VIES) and how to opt into additional slices via the include parameter. It also describes error handling (returns clean dict). However, it does not explicitly contrast with sibling tools like 'sugra_entity_screen' or 'fetch_data' for when to choose this tool over them.

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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