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LiveDataLink

by io.github.blackboxfoundry

carrier_vetting_score

Read-onlyIdempotent

Vet a US motor carrier (trucking company) for a freight-brokerage 'is this safe and legit to broker a load to?' decision, in one call. Give a USDOT number, MC number, or carrier name. Joins FMCSA safety data (operating authority, insurance on file, BASIC safety scores, crash and out-of-service history vs the national average, safety rating) with sanctions screening (OFAC/UN/EU/BIS on the carrier's legal name), federal-court litigation history (CourtListener), and USAspending federal awards. Returns a GO / CAUTION / NO-GO verdict with the reasons, plus all the underlying fields. NO-GO is triggered by a sanctions match or lack of active operating authority; CAUTION by BASIC alerts, fatal crashes, missing liability insurance, high out-of-service rates, or litigation. This is an analytical aid, not a substitute for your own carrier-onboarding checks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
mcNoMC (motor carrier) number, without the 'MC-' prefix. Used if no DOT number is given.
dotNoUSDOT number of the carrier (most precise). Example: 76830.
nameNoCarrier legal name, used if no DOT/MC number is given. The top FMCSA name match is vetted; prefer a DOT number for an exact carrier.

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, and the description adds substantial behavioral detail: the joined data sources, the returned verdicts, and the precise triggers for NO-GO and CAUTION. It also sets expectations that this is an analytical aid, not a substitute for the broker's own onboarding checks.

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 detailed but efficient: it front-loads the purpose and inputs, then covers data sources, return format, decision logic, and a disclaimer. Every clause carries decision-relevant information, with no filler or repetition of schema content.

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 and the absence of an output schema, the description adequately explains what is returned, what data sources are joined, and how the verdict thresholds work. An agent has enough information to invoke it correctly with any of the three identifiers and to interpret the result.

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?

The input schema already documents all three parameters with 100% coverage, including identifier precedence and the recommendation to prefer a DOT number for exact matches. The description only restates that a DOT, MC, or name can be supplied, adding little beyond what the schema provides.

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 specifies a concrete verb ('vet'), a clear resource ('US motor carrier'), and the exact decision outcome (GO / CAUTION / NO-GO). It frames the tool for a freight-brokerage 'is this safe and legit to broker a load to?' question and signals a single-call scored assessment, distinguishing it from batch or evidence-pack siblings.

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 explicitly states the use case — vetting a carrier for a freight-brokerage decision — and lists the three acceptable identifier types. It does not explicitly compare against sibling tools such as carrier_vetting_batch or carrier_monitor_recheck, but the context is clear enough for an agent to select this tool.

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

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