@aiwerk/mcp-server-wheel-size

list-generations

Read-only

Retrieve body generation slugs for a specific make, model, and year. These slugs are required inputs for listing modifications and classifying wheel fitment data.

Instructions

List all body generations for a given make/model/year combination. Returns generation slugs (e.g., "b9" for Audi A4 2020) used as the "generation" input for list-modifications and classified tools.

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>",
      "model": "<model>",
      "year": "<year>"
    },
    "name": "list-generations"
  }
}

Input Schema

Name	Required	Description
`make`	Yes	Manufacturer slug (e.g., "audi", "bmw"). Get slugs from list-makes.
`year`	Yes	4-digit model year 1900-2100 (e.g., 2020). Get valid years from list-years.
`model`	Yes	Model slug (e.g., "a4", "3-series"). Get slugs from list-models.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.3Aug 1, 2026

TDQS

A4.2/5.0

Behavior4/5

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

The annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds behavioral context: it returns 'all body generations' (no filtering) and provides an example slug format ('b9'), which is helpful for understanding the output without an output schema.

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 sentence states the primary purpose, and the second explains the output format and downstream usage. No filler, redundant information, or unnecessary details.

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?

The description covers the core action, return format with an example, and downstream consumers. For a read-only list tool without an output schema, it provides sufficient context. It does not mention edge cases like no matching generations, but the overall scope is adequate given the schema and annotations.

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 is fully descriptive (100% coverage), with each parameter explaining how to obtain valid slugs. The description adds an example generation slug but does not add per-parameter semantics beyond what the schema provides. Baseline 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 states 'List all body generations for a given make/model/year combination' with a specific verb ('List'), resource ('body generations'), and scope. It also distinguishes itself from sibling tools by explaining the output are generation slugs used as input for downstream tools like list-modifications.

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 clear context for when to use this tool: it returns generation slugs needed for list-modifications and classified tools. It does not explicitly list alternatives or exclusions, but the downstream usage makes the purpose evident among many sibling list tools.

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