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Sugra-Systems

Sugra API MCP

Official

sugra_entity_lookup

Read-onlyIdempotent

Resolve legal entities by LEI or VAT and get their KYB envelope: sanctions screening, status, and optional ownership and adverse-media data.

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
valueYesThe identifier value: 20-character LEI or the VAT number.
anchorYesIdentifier type: lei (GLEIF) or vat (EU VIES).
includeNoOptional fuller slices to add, e.g. ownership, adverse_media. Omit for the compact default. profile and screening are already in the compact core and are not extra slices.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.11.0
  2. Removedv0.9.1
  3. First observedv0.8.2

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already signal read-only/idempotent behavior, and the description adds substantial behavioral detail: compact-by-default output to protect context budget, a clean {error, detail} dict instead of exceptions, always-present disclaimer, and the caveat that screening is a signal rather than a compliance determination. This goes well beyond the annotations.

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 front-loaded with purpose, then gives the output contract, error behavior, and parameter semantics in a clear, labeled structure. Every sentence earns its place without padding.

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?

For a read-only lookup tool, the description covers input constraints, return shape, optional parameters, error behavior, and important caveats. With the annotations and output schema available, nothing essential is missing for an agent to call 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?

Even though schema coverage is 100%, the description adds real semantic value: it explains that lei resolves via GLEIF and vat via VIES, specifies the value formats, and clarifies that include opts into fuller slices while the default is compact. This materially improves correct invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb ('Resolve an entity by identifier') and names the concrete deliverable ('composed KYB envelope'), so an agent can tell what the tool does. However, it does not explicitly distinguish this tool from siblings like sugra_entity_screen or resolve_entity, so it misses the top differentiator.

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?

It gives clear usage context: identifier-based lookup with lei/vat, optional include slices, and the compact-vs-full output behavior. It also tells the agent how to handle errors by branching on result.get('error'). It does not state when not to use this tool or name an alternative, but the guidance given is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.