Skip to main content
Glama

Sugra API

by ai.sugra

sugra_entity_screen

Read-onlyIdempotent

Screen a person or organization name against the Sugra sanctions corpus.

Returns a SCREENING SIGNAL, not a compliance determination. Sugra is a technology provider, not a sanctions authority or consumer reporting agency. PEP and adverse-media coverage is supplementary and non-comprehensive - a clear result is not proof of absence, and a hit is a candidate match to review, not a finding.

Output is COMPACT to protect the agent context budget: {status, matches:[{name, score, list, type}], disclaimer}. The verdict status is one of clear, review, or hit. The heavy raw fields (match rationale, source ids, publish dates) are dropped; use the Sugra API directly when the full screening envelope is needed.

Args: name: The person or organization name to screen (required). country: Optional ISO 3166-1 alpha-2 country to narrow the match. dob: Optional date of birth (YYYY-MM-DD) for a person. nationality: Optional nationality to narrow the match.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dobNo
nameYes
countryNo
nationalityNo

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?

Annotations indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds important behavioral context: output is compact to protect context budget, results may be incomplete (open-world), and PEP/adverse-media is supplementary and non-comprehensive. 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 with clear paragraphs, first stating purpose, then behavioral notes, then output format, then parameter descriptions. It is concise without excess, though the parameter list could be more terse.

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?

Given the tool's complexity (sanctions screening) and the presence of annotations and an output schema, the description covers essential aspects: input parameters, output verdicts, limitations, and use cases. It is complete enough for an agent to use 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?

With schema description coverage at 0%, the description meaningfully explains each parameter: name (required), country (ISO 3166-1 alpha-2), dob (YYYY-MM-DD), nationality. This adds format and optionality hints beyond the schema's basic type information.

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 a name against the Sugra sanctions corpus, specifying the verb 'screen' and the resource. It distinguishes from siblings like 'sugra_entity_lookup' by focusing on screening for sanctions hits.

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 states it returns a screening signal, not a compliance determination, and advises that Sugra is not a sanctions authority. It also explains when to use the full API for more detail, providing clear context for agent decision-making.

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

All Sugra API toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources