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check_cdl_requirement

Read-only

Whether a bus needs a commercial driver's license under US federal rules, which class (B or C), and which endorsements (passenger, school bus), from the passenger count, weight rating and whether it carries students. Returns a link to buses for sale in that size. Federal only; states can add rules.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "passengers": "<passengers>"
    },
    "name": "check_cdl_requirement"
  }
}

Input Schema

NameRequiredDescriptionDefault
weightNoGross vehicle weight rating, from the door-frame label. Default unknown.
passengersYesPassenger seats, not counting the driver. Count each wheelchair position.
carries_studentsNotrue if it will carry students to or from school or school events.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already cover read-only/safe behavior, so the marginal bar is lower; the description adds real context: it discloses the rule basis (US federal CDL rules), the output shape (class B/C, endorsements), and an important limitation (federal only, states can add rules). The odd side effect of returning a bus-for-sale link is disclosed, though not explained.

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 tight sentences, front-loaded with the question the tool answers and the outputs. The trailing 'Returns a link to buses for sale' sentence is slightly puzzling but earns a place by disclosing an unexpected return.

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 read-only, single-purpose classifier with fully documented params and no output schema, this is nearly complete: rule basis, outputs, and federal-only limitation are all stated. Missing only explicit guidance on the state-law edge case and what the response looks like.

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 coverage is 100% and the parameter descriptions are already precise (seats excluding driver, wheelchair positions counted, weight from door-frame label). The description only restates that these inputs drive the result, adding no format or edge-case detail beyond the schema; baseline 3 applies.

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?

States a specific verb (check/classify) and resource (CDL requirement for a bus), plus the exact outputs: needs-CDL yes/no, class B or C, and endorsements. Clearly distinct from siblings like check_vehicle_rules or get_used_bus_prices.

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

Usage Guidelines3/5

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

Implied usage (from passenger count, weight, and student carriage) but no explicit when-to-use or when-not, and no routing to the closest sibling check_vehicle_rules. The 'Federal only; states can add rules' caveat hints at scope limits but doesn't say what to do about states.

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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