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by io.github.blackboxfoundry
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carrier_vetting_score

Read-onlyIdempotent

Vet a US motor carrier (trucking company) for a freight-brokerage review, in one call. Give a USDOT number, MC number, or carrier name. Joins FMCSA public safety data (operating authority, insurance on file, crash and out-of-service history vs the national average, safety rating, and CSA BASIC indicators where FMCSA publishes them) 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 decision-support result with reasons and the underlying fields. NO-GO is triggered by a sanctions match or lack of active operating authority; CAUTION by published BASIC alerts, fatal crashes, missing required liability insurance, high out-of-service rates, litigation, or an unavailable source. 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • changedInput schema / properties / dot / description
      Previous value: -"USDOT number (preferred). Accepts 76830 or USDOT 76830."New value: +"USDOT number of the carrier (most precise). Example: 76830."
    • changedInput schema / properties / dot / type
      Previous value: -[
-  "number",
-  "string"
-]New value: +"number"
    • changedInput schema / properties / mc / description
      Previous value: -"MC number. Accepts 123456 or MC-123456."New value: +"MC (motor carrier) number, without the 'MC-' prefix. Used if no DOT number is given."
    • changedInput schema / properties / mc / type
      Previous value: -[
-  "number",
-  "string"
-]New value: +"number"
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

The description explains the decision logic: what triggers NO-GO (sanctions match or lack of active operating authority), CAUTION (BASIC alerts, fatal crashes, missing insurance, high out-of-service rates, litigation, unavailable source), and GO (implicitly). It also discloses that it joins multiple data sources and adds a disclaimer that it's analytical aid, not a substitute for own checks. The annotations already indicate readOnlyHint=true, so no contradiction. It doesn't discuss rate limits or performance, but the decision logic is well covered.

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 well-structured and front-loads the purpose in the first sentence. It is concise but dense with useful information: input requirements, data sources, decision categories, triggers, and disclaimer. Every sentence earns its place, with no redundant or filler content. It effectively balances thoroughness and brevity.

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 (aggregating multiple data sources with decision logic), the description is highly complete. It covers all key aspects: what inputs to provide, what data is joined, what outputs (GO/CAUTION/NO-GO with reasons), and critical triggers. It also provides a disclaimer about its use as analytical aid. No output schema exists, but the description sufficiently explains the returned result's nature. No gaps are apparent for an agent to call it correctly.

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 coverage is 100%, so each parameter already has a description. The tool description adds context: it explains that DOT is most preciseions, MC is used if no DOT, and name is less precise with top match vetted. This enhances the parameter semantics by clarifying preference order and fallback behavior, but it doesn't add extensive new meaning beyond schema. Baseline 3 is appropriate because schema fully covers parameters and description adds limited extra guidance.

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 explicitly states the purpose: 'Vet a US motor carrier (trucking company) for a freight-brokerage review, in one call.' It specifies the resource (US motor carrier) and the action (vet), and distinguishes it from siblings like carrier_vetting_batch and carrier_vetting_evidence_pack by noting it returns a decision-support result in one call, while siblings imply batch or evidence pack functions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly defines when to use the tool for a single carrier vetting, and it implicitly differentiates from siblings: 'in one call' suggests the batch version for multiple carriers. It also provides guidance on parameter selection: 'Give a USDOT number, MC number, or carrier name' and notes that DOT is most precise, MC is used if no DOT, and name is last resort with top match vetted. This is explicit usage context.

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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