Skip to main content
Glama

LiveDataLink

by io.github.blackboxfoundry

fmcsa_carrier_compare

Read-onlyIdempotent

Compare 2 to 5 trucking companies side by side on safety, fleet size, insurance, and authority. Returns a comparison table: fleet size, driver count, safety rating, crash history, BASIC safety scores, authority status, insurance, and out-of-service rates. Use this for questions like 'which carrier is safer?', 'compare these trucking companies', 'which freight company should I use?', 'evaluate these carriers against each other', 'help me pick between these haulers', or any carrier vetting decision.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dot_numbersYes2-5 USDOT numbers, as an array or comma-separated string.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds value by disclosing the comparison-table output and the specific data points returned (fleet size, driver count, BASIC scores, out-of-service rates), which is especially important given there is no output schema.

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?

Two sentences: the first front-loads the core purpose, the second specifies the return table and then lists concrete example phrasings. Every sentence earns its place, and the example queries serve as useful routing signals for an agent.

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 tool with a single required parameter and a fully documented schema, the description is largely complete: it defines the input scope, the output format, and typical intents. It could add minor context such as error behavior or prerequisite lookup via fmcsa_carrier_search, but nothing critical is missing for correct invocation.

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 schema description fully covers the dot_numbers parameter (2-5 USDOT numbers, array or comma-separated string), so the baseline is 3. The tool description reinforces the range (2 to 5 companies) but does not add meaning beyond the schema, such as how to obtain DOT numbers or what happens with invalid entries.

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 opens with a specific verb and target: 'Compare 2 to 5 trucking companies side by side' on defined dimensions such as safety, fleet size, insurance, and authority. It clearly differentiates this from sibling tools like fmcsa_carrier_lookup and fmcsa_carrier_search by emphasizing multi-company side-by-side comparison.

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 explicit use-case examples ('which carrier is safer?', 'compare these trucking companies') and states 'or any carrier vetting decision', providing strong context for when to invoke the tool. It does not explicitly name alternatives or exclusion criteria, but the illustrative questions make the intended use unambiguous.

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