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Sugra API

by ai.sugra

sugra_entity_lookup

Read-onlyIdempotent

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

TDQS

A4.7/5.0
Behavior5/5

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

The description goes beyond annotations by clarifying that the screening verdict is a signal, not a compliance determination, and that PEP/adverse-media content is non-comprehensive. It also explains the compact default to protect context budget and the error handling behavior. No contradictions with annotations.

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?

The description is well-structured and informative, but slightly verbose. It front-loads the purpose and provides detailed parameter and output explanations. Could be trimmed slightly without losing clarity.

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, the description covers purpose, all parameters, output format (with and without include), error handling, and disclaimers. With an output schema present, the description is thorough and leaves no major gaps.

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 compensates fully by explaining the meaning of each parameter (anchor types, value format, include slices) and providing examples. This adds significant value beyond the bare schema.

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 resolves an entity by identifier and returns a composed KYB envelope. It specifies the anchor types (lei, vat) and outlines the result contents, making the purpose very specific and actionable.

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 provides guidance on when to use the tool (for entity resolution with optional slices) and how to control output via the include parameter. However, it lacks explicit differentiation from sibling tools like resolve_entity or sugra_entity_screen, which would strengthen the when-not-to-use advice.

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

A3.7/5.0
Disambiguation3/5

Several tools overlap in purpose: call_endpoint, fetch_data, and search_endpoints all relate to invoking endpoints, with fetch_data bundling search and call. resolve_entity and sugra_entity_lookup both resolve entities but target different domains (market vs. compliance), which could confuse agents.

Naming Consistency2/5

Naming patterns are inconsistent: 'endpoint' appears as both singular and plural (call_endpoint vs. search_endpoints), verbs vary (fetch_data vs. get_snapshot), and the 'sugra_' prefix is only applied to two of the entity-related tools, leaving resolve_entity without a clear thematic connection.

Tool Count4/5

With 11 tools, the count is within the typical range for a comprehensive financial API wrapper and does not feel bloated or sparse. Each tool serves a distinct functional area, so the number is appropriate.

Completeness4/5

The tool set covers endpoint discovery, data retrieval (snapshots, timeseries), entity resolution, and compliance screening, which are the core capabilities expected of such an API. Minor gaps like batch operations or authentication handling are not critical for the intended use case.

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