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classify_vehicle

Classify a Brazilian light electric vehicle under Resolução Contran 996/2023 art. 2 and tell whether it needs a licence plate. Categories: bicicleta_eletrica, autopropelido (no plate), ciclomotor (plate + ACC or CNH A), motocicleta (plate + CNH A).

Input Schema

NameRequiredDescriptionDefault
rodasNoNumber of wheels
tem_pedalNoPedal drives the wheel (true = pedal-assist bicycle)
largura_cmNoVehicle width in cm (needed for autopropelido)
potencia_wYesNominal motor power in watts
entre_eixos_cmNoWheelbase in cm (needed for autopropelido)
tem_aceleradorNoHas a throttle?
velocidade_kmhYesFactory maximum speed in km/h

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does disclose the substantive logic: the four output categories and whether each requires a plate (plus ACC or CNH A). What is missing is anything about error handling or how borderline/ambiguous inputs are resolved, so it is strong but not exhaustive.

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 sentences, front-loaded with the purpose and regulation before the category/plate mapping. Dense but every element (verb, regulation, categories, plate consequence) earns its place; only the long final enumeration slightly reduces readability.

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 no output schema and no annotations, the description conveys what is returned (a category and a plate requirement) and the legal basis. It stops short of describing the response shape or handling of ambiguous inputs, but an agent has enough 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?

Schema description coverage is 100%, so all seven parameters are already documented in the schema, including which fields are 'needed for autopropelido'. The description adds no additional parameter meaning or format guidance beyond that, so the 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 (classify) and resource (Brazilian light electric vehicle) plus the exact legal instrument, Resolução Contran 996/2023 art. 2. The category enumeration with plate requirements makes it unmistakably distinct from calculate_range, compare_models, list_models and search_models.

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?

Usage is implied by the domain: this is the only regulatory classifier among model search/compare/range siblings, so an agent can infer when it applies. However there is no explicit statement of when to pick this over the sibling tools or what inputs are needed to qualify a vehicle.

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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