Skip to main content
Glama

LiveDataLink

by io.github.blackboxfoundry

company_trust_check

Read-onlyIdempotent

One-call public-record trust read for a company - answers 'can I trust this company?' for KYB/vendor-diligence agents. Joins the LiveDataLink sources that carry a legitimacy or risk signal into a single standing read: an OFAC SDN / EU / UN / BIS sanctions screen (a hit is decisive), SEC EDGAR registration plus the latest filing date (legitimacy + recency), USAspending federal award history (the US government transacts with them), and EPA ECHO facility compliance (significant-non-compliance, penalty, and non-compliant-quarter flags). Returns a plain-English verdict band (ESTABLISHED PUBLIC-RECORD STANDING / STANDING WITH FLAGS / LIMITED FOOTPRINT / SANCTIONS SCREEN HIT), the positive signals, the flags to review, and the resolved identifiers. A source that fails is noted, not fatal. This is a public-record SIGNAL read, not a credit report, background check, or endorsement: absence of a record is not proof of wrongdoing and a match is not proof of guilt. Premium cross-source synthesis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNoOptional 2-letter state to disambiguate the EPA facility search.
tickerNoOptional stock ticker hint to pin the SEC EDGAR match (e.g. 'LMT').
companyYesCompany or organization name to check (e.g. 'Lockheed Martin', 'Acme Widgets LLC').

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already provide readOnly, openWorld, idempotent, and non-destructive hints. The description adds meaningful behavioral context beyond those hints: a sanctions hit is decisive, failing sources are noted but not fatal, and the read returns a verdict band plus signals, flags, and identifiers. It also clarifies the interpretative limits of absence and match.

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 longer than average but front-loads the purpose and uses each sentence for a distinct job: scope, sources, output, failure behavior, caveats, and positioning. A few phrases like 'premium cross-source synthesis' add limited value, but there is no wasteful 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?

Despite having no output schema, the description explains exactly what the caller receives: a verdict band, positive signals, flags to review, and resolved identifiers. It also covers source-failure handling and interpretive caveats, so combined with the parameter schema and annotations, the agent has enough context to invoke and interpret the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameters company, state, and ticker are already documented. The description does not add new parameter-level details beyond connecting the overall tool to its data sources; it stays at the baseline of relying on the schema for parameter semantics.

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 leads with a specific verb and resource: a one-call public-record trust read for a company, explicitly answering 'can I trust this company?' for KYB/vendor-diligence agents. It names the exact data sources joined and the verdict bands returned, making the tool's scope unmistakable and distinct from generic company-info or sanctions tools.

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 intended use case is clearly stated (KYB/vendor-diligence trust questions) and the description adds exclusions: it is not a credit report, background check, or endorsement, and absence of a record is not proof of wrongdoing. It does not name a specific sibling tool as the alternative, but the context and boundaries are clear.

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

All LiveDataLink 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

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

Resources