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LiveDataLink

by io.github.blackboxfoundry

fmcsa_carrier_authority

Read-onlyIdempotent

Check if a trucking company is legally authorized to operate and has valid insurance. Returns operating authority status (common, contract, broker - active/inactive/revoked), BIPD insurance, cargo insurance, bond/surety status, and whether they're allowed to haul freight. Use this for questions like 'can this carrier legally operate?', 'do they have insurance?', 'is this broker licensed?', 'verify carrier authority', 'check trucking company credentials', 'is this freight company legit?', or any carrier compliance check.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dot_numberYes

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context by specifying the types of legal/insurance statuses checked and returned. 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 front-loaded with the core purpose, then return details, then usage examples. The example query list is somewhat redundant but serves intent matching. No wasted filler, and the structure logically flows from function to data to usage.

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?

For a single-parameter read-only tool, the description covers the operation, the returned data categories, and when to use it. No output schema exists, but the description lists the return fields. The primary gap is missing dot_number parameter guidance, though the parameter name and schema type provide partial context.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for the undocumented dot_number parameter. It does not mention dot_number, how to obtain it, or any format guidance beyond what the schema already provides. The description's focus on trucking companies only implicitly relates the parameter, which is insufficient.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Check') and resource (trucking company legal authority and insurance), and enumerates the return fields (operating authority status, BIPD insurance, cargo insurance, bond/surety status). It is clear what the tool does, though it does not explicitly distinguish itself from sibling tools by name.

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 clear usage context with concrete example queries ('can this carrier legally operate?', 'is this broker licensed?') and a catch-all ('or any carrier compliance check'). It lacks explicit guidance on when not to use this tool versus alternatives like fmcsa_carrier_lookup or fmcsa_carrier_search.

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