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fmcsa_carrier_search

Read-onlyIdempotent

Search for trucking companies, freight carriers, or motor carriers by company name. Find any carrier's DOT number, MC number, location, fleet size, and operating status. Supports partial name matching. Use this for questions like 'find this trucking company', 'what's the DOT number for Werner?', 'search for freight carriers in Texas', 'look up this logistics company', or any carrier name search. Returns up to 50 matching carriers from the FMCSA national database.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes

TDQS

A4/5.0
Behavior4/5

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

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds useful behavioral details beyond those: partial name matching, a result cap of 50, and the source as the FMCSA national database. This is meaningful context and does not contradict the 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 action and resource, then gives examples and a result limit. The example list is a bit repetitive with the closing 'or any carrier name search,' but every sentence adds practical value and the structure is easy to scan.

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?

Even without an output schema, the description lists the key returned fields (DOT number, MC number, location, fleet size, operating status) and the 50-result cap. For a simple name-search tool with one parameter, this is sufficient for an agent to understand what it will get and how results are bounded.

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?

The schema only defines a single 'name' parameter with no description, so the description carries the burden. It clearly explains that the parameter is a company name, supports partial matching, and lists example queries. It could add case-sensitivity or formatting details, but for a single self-explanatory parameter, it compensates well.

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 uses a specific verb and resource: 'Search for trucking companies, freight carriers, or motor carriers by company name.' It also clarifies the scope by stating it retrieves DOT number, MC number, location, fleet size, and operating status. However, it does not explicitly differentiate this search tool from the closely related fmcsa_carrier_lookup or fmcsa_carrier_authority siblings, so it falls short of a 5.

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 gives clear usage context with concrete example queries ('find this trucking company', 'what's the DOT number for Werner?') and explicitly frames it as 'any carrier name search.' It does not state when to avoid this tool in favor of an exact DOT/MC lookup or another sibling tool, so it lacks explicit exclusions.

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.

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