@aiwerk/mcp-server-wheel-size

list-years

Read-only

Retrieve all model years for a given manufacturer, providing the year values needed to query models, generations, and tires.

Instructions

List all model years available for a given manufacturer. Use the year values returned here as the "year" input for list-models, list-generations, and search-by-model.

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": {
      "make": "<make>"
    },
    "name": "list-years"
  }
}

Input Schema

Name	Required	Description	Default
`make`	Yes	Manufacturer slug (e.g., "audi", "ford", "toyota"). Get slugs from list-makes.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.3Aug 1, 2026

TDQS

A4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description does not need to restate safety. It adds the behavioral context that the tool returns all available years and that these values serve as inputs to other tools, which is useful. However, there is no extra detail on pagination, rate limits, or data format, leaving some transparency gaps.

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 two sentences: the first states the purpose, the second explains downstream usage. It is front-loaded with the primary action and contains no filler or redundant information. Every word earns its place.

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 simple list tool with one well-documented parameter and no output schema, the description provides the essential context: what it returns (all model years) and how to use the results. It lacks explicit return-format details, but the tool's simplicity makes this a minor gap. Overall it is adequately complete.

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?

The input schema fully describes the 'make' parameter, including examples and a pointer to list-makes for valid slugs. The description only says 'given a manufacturer,' which adds no meaningful semantics beyond the schema. Since schema coverage is 100%, a baseline of 3 is appropriate.

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 clearly states the tool lists all model years for a given manufacturer, using a specific verb ('List') and resource ('model years'). It distinguishes itself from sibling list-* tools by its focus on years and explicitly ties its output to downstream tools (list-models, list-generations, search-by-model).

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 explains how to use the tool's output ('Use the year values returned here as the "year" input') for three specific sibling tools, which provides clear context on when to invoke this tool. It does not explicitly state exclusions or alternative approaches, but the downstream usage guidance is strong for a simple lookup tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.