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Get the specifications of a generation

get_vehicle_specs
Read-onlyIdempotent

Use this for the specifications of one generation: horsepower, torque, engine type, transmission, drivetrain, weight, dimensions, fuel economy, tire sizes and new-car prices. First find the car with search_vehicles, then pass its model_id. Returns {model, engines, minor_changes, total, grades, next_offset}. engines lists each engine version fitted to the generation, with code, family, name (the version, null when the engine has only one), cylinders, layout, displacement_cc, aspiration, max_power_ps and max_power_kw (both as the source states them, never converted; null means the source did not state it), max_power_rpm (with max_power_rpm_high for a range), max_power_is_lower_bound (true means 'or more'), max_torque_nm, max_torque_kgfm, max_torque_rpm, and the engine page url. minor_changes lists the revisions of the generation (id, name, start and end year and month) oldest first. grades are the catalog rows, each with minor_change_id (the revision it belongs to), name, transmission, drivetrain, displacement_cc, engine_code, max_power and max_torque (as written in the catalog) with max_power_ps and max_torque_nm, weight_kg, length_mm, width_mm, height_mm, fuel_economy_km_l, price_yen, tire sizes, and special_edition (true for a special edition listed as a grade). Output can differ between engines and grades, because special editions and revisions change it; when they differ, give the grade values for that grade. Grades are cut to fit the response size; when next_offset is not null, call again with offset set to it. An empty engines, minor_changes or grades list means not researched yet, never none. Unknown ids are an error, not an empty result.

Input Schema

NameRequiredDescriptionDefault
offsetNoWhere to start in the list; pass next_offset from the previous response. Omit to start from the beginning.
model_idYesGeneration (model) id (a UUID from search_vehicles).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds high-value behavior beyond that: empty lists mean 'not researched', unknown ids are an error rather than an empty result, grades are truncated for response size, and null fields mean the source omitted the value. These are non-obvious semantics an agent would otherwise get wrong.

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?

Front-loaded with purpose and workflow before the long return-field enumeration. The field list is verbose, but with no output schema it is largely load-bearing rather than padding; only minor tightening is possible.

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?

With no output schema and only two params, the description carries the full burden and does so: it documents the return object shape, per-field meaning and null conventions, pagination via next_offset, and the empty-vs-error distinction. Nothing needed to call it correctly is missing.

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?

Schema coverage is 100%, so baseline is 3, but the description adds real meaning: model_id is a generation id obtained from search_vehicles, and offset should be the next_offset from the prior response. The source-provenance notes (values as stated, never converted) further clarify parameter/return semantics.

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?

Opens with a specific verb+resource and enumerates exactly the data returned (horsepower, torque, engine type, dimensions, fuel economy, prices), so the agent knows precisely what this tool yields. It also names search_vehicles as the lookup counterpart, distinguishing its role from the acquisition step.

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?

Gives an explicit workflow: find the car with search_vehicles, then pass its model_id, plus concrete pagination instructions (call again with offset set to next_offset). It does not explicitly contrast against sibling get_vehicle, so the when-not-to-use case is left implicit.

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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