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LiveDataLink

by io.github.blackboxfoundry

fmcsa_carrier_lookup

Read-onlyIdempotent

Look up a trucking company, freight carrier, or motor carrier by DOT number or MC number. Returns company name, address, phone, fleet size, number of drivers, safety rating, operating authority, insurance status (BIPD, cargo, bond), crash history, inspection rates, and out-of-service percentages. Use this for questions like 'is this carrier safe?', 'look up this trucking company', 'check this DOT number', 'verify this carrier', 'what's their safety rating?', or any freight carrier lookup. Covers all US carriers registered with FMCSA.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
mc_numberNo
dot_numberNo

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already provide the safety profile (readOnly, openWorld, idempotent, non-destructive), so the description does not need to repeat it. It adds useful context about result contents and registry coverage, but no additional behavioral risks, limits, or error conditions.

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?

Two sentences, front-loaded with the action and output, followed by representative queries and scope. The trigger list is useful for routing; it is long but earns its place.

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?

The description discloses the result fields, registry scope, and appropriate question forms, which is sufficient for an agent to select and invoke the tool. The main gap is not stating minimum identifier requirements, but the 'by DOT number or MC number' phrasing implies one must be supplied.

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 has no parameter descriptions (0% coverage), but the description identifies the two identifiers as DOT number or MC number, matching mc_number and dot_number. It does not clarify that at least one is effectively required or what happens when both are supplied, leaving part of the semantic burden unaddressed.

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?

Description opens with a specific verb and resource: 'Look up a trucking company... by DOT number or MC number,' and enumerates the returned data categories. This clearly distinguishes it from sibling search/compare/authority/safety-score tools, even though those siblings are not named.

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?

Gives explicit trigger phrases ('is this carrier safe?', 'check this DOT number') and states it covers all US FMCSA-registered carriers. It does not name sibling alternatives or exclude name-based queries, so it is clear context without full when-not/alternative routing.

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