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driveate

wheel-size-mcp

by driveate

Catalog (freely callable)

ws_list_modifications
Read-onlyIdempotent

Find vehicle trims and engine variants by make and model, filtering by year, generation, or horsepower to get production years and specs.

Instructions

List modifications (trims) for a specific vehicle.

Returns trim names, engine specs, and production years. One of year or generation is required. Filter by power via horsepower (exact ±2.7 hp) or horsepower_min/max (e.g. "trims over 300 hp" → horsepower_min=300). After getting a modification slug, use ws_search_by_vehicle for fitment data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fuelNoFuel type filter (e.g. 'diesel', 'electric', 'hybrid', 'petrol')
langNoTranslate names (e.g. 'ru'). name_en keeps the English original.
makeYesMake slug
trimNoFuzzy engine/trim name search (e.g. '2.0T', 'V6')
yearNoModel year
modelYesModel slug
regionNoRegion slug(s) (e.g. ['usdm'] or ['eudm', 'audm']). Multiple regions give a more comprehensive view.
generationNoGeneration slug (alternative to year)
horsepowerNoHorsepower (±2.7 hp band, e.g. 150)
trim_levelNoCase-insensitive trim level (e.g. 'EX-L', 'Touring', 'Sport')
horsepower_maxNoMaximum horsepower
horsepower_minNoMinimum horsepower (e.g. 300)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context such as the ±2.7 hp tolerance for horsepower, the constraint that year or generation is required, and the note that returned slug leads to fitment data. No contradictions with annotations.

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 four sentences, front-loaded with the core purpose, then returns data, constraints, and an example. Every sentence contributes meaningful guidance without repetition or fluff.

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 12 parameters, 2 required, and an output schema, the description covers the essential decision points: what the tool returns, required inputs, filtering options, and the next step. It is sufficient for an agent to select and invoke the tool correctly without additional context.

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 description coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by clarifying horsepower filtering ('exact ±2.7 hp' and the min/max relationship), and emphasizing the year/generation requirement. This enriches the agent's understanding of how to combine parameters.

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 begins with a specific verb and resource: 'List modifications (trims) for a specific vehicle.' It clearly states the returned data (trim names, engine specs, production years) and distinguishes itself from sibling ws_search_by_vehicle by pointing to that tool for fitment data after obtaining a slug.

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 usage guidance: 'One of year or generation is required' and provides a concrete filtering example for horsepower. It also names the next logical tool (ws_search_by_vehicle) for fitment data. It does not explicitly state when not to use the tool compared to other listing tools, but the context is sufficient.

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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