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driveate

wheel-size-mcp

by driveate

Catalog (freely callable)

ws_list_makes
Read-onlyIdempotent

List every vehicle make (brand) with its slug and name. Begin fitment lookups by narrowing results via year, region, or specific brand slugs.

Instructions

List all vehicle manufacturers (makes).

Returns slugs and names for all car brands in the database.

Common starting point for vehicle fitment lookups, but not the only one — ws_list_years can also be called first (without params) to start from year.

After getting a make slug, use ws_list_models to find models.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoTranslate names (e.g. 'ru'). name_en keeps the English original.
yearNoFilter by year (e.g. 2024)
brandsNoOnly these make slugs (e.g. ['toyota', 'nissan']). For curated storefronts.
regionNoRegion slug(s) (e.g. ['usdm', 'jdm']). Filter makes sold in these regions.
brands_excludeNoExclude these make slugs (e.g. ['geely', 'great-wall']).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

Description mentions return content (slugs and names) but output schema likely covers this. Annotations already declare readOnly and idempotent, so description adds little beyond workflow context; no contradictions.

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?

Three sentences, front-loaded purpose, then alternative and next step; no filler.

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 simplicity, optional parameters fully described in schema, output schema present, and annotations covering safety, the description provides sufficient context for selection and invocation.

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 covers 100% of parameters with descriptions, so description needn't explain them; it adds workflow context but no param specifics.

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?

Clearly states it lists all vehicle manufacturers (makes) and returns slugs and names, distinguishing itself from sibling list tools like ws_list_years and ws_list_models by describing its role as a starting point.

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?

Explicitly says it is a common starting point but not the only one, naming ws_list_years as an alternative entry point, and instructs to use ws_list_models after obtaining a make slug.

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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