TiresVote MCP

Official

by driveate

Overview Schema Source Related Servers Discussions Admin

Python

Remote

Catalog (freely callable)

tires_list_brand_tires

Read-onlyIdempotent

List tire models for one brand by slug, returning names, seasons, types, and status flags. Filter by region, season, runflat, or vehicle class to find relevant models.

Instructions

List tire models of one brand.

Rows carry slug/display, canonical_link, season and automobile-type slugs, year, status flags (discontinued, almost_discontinued, coming_soon, is_runflat), counters and region slugs. An unknown brand slug returns an empty list with total: 0 (upstream answers 200, not 404).

Upstream returns at most 200 models for one brand. total is the upstream count, available_count what was actually fetched; when truncated is true, part of the brand's catalog is unreachable here — narrow filters or use tires_search_advanced(brands=[...]) which paginates properly. has_more/next_offset page only within the fetched set. counters.modes may read 0 although variants exist — verify with tires_list_sizes, never infer absence. Per-row regions cap at 10 slugs (regions_more counts the rest; the model's canonical_link lists all markets).

Input Schema

TableJSON Schema

Name	Required	Description
`brand`	Yes	Brand slug from tires_list_brands or a search/card response, e.g. 'michelin'. A brand's display name is not a slug.
`limit`	No	Max models per slice (1–50). Default 20.
`offset`	No	Skip this many models (0-based); follow next_offset.
`regions`	No	Keep only models sold in these TiresVote market regions (repeated 'region' params, at most 20 — an MCP-side bound). Resolve slugs via tires_list_regions, e.g. ['eudm', 'usdm']. Omit for all markets.
`runflat`	No	RunFlat filter for the brand catalog (distinct from tires_search_advanced.runflat_filter): true keeps ONLY RunFlat models (live-verified). Omit for no RunFlat constraint; explicit false applies the upstream default, it does not exclude RunFlat.
`seasons`	No	Keep only these seasons: 'summer', 'all' (all-season), 'winter' (repeated 'season' params). Omit for all seasons.
`ordering`	No	Sort order, comma-separated without spaces; fields: 'popularity', 'score', 'slug', each optionally prefixed with '-' for descending. Upstream default: '-popularity,-score,slug'.
`include_oe`	No	true also lists OE (original equipment) models. Sent as 'show_oe'.
`automobile_type`	No	Vehicle class filter: 'car' (passenger) or 'suv' (light truck/SUV).
`include_discontinued`	No	true also lists discontinued models (default hides them). Sent as 'show_discontinued'.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 27, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations only cover the safety profile (readOnly/idempotent/non-destructive); the description adds substantial behavior beyond them: a hard 200-model upstream cap, the total vs available_count vs truncated distinction, the fact that has_more/next_offset only page within the fetched set, the 200-not-404 behavior for unknown brand slugs, a counters caveat that can under-report, and the 10-slug regions cap with regions_more. This is unusually rich operational disclosure.

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?

Purpose is front-loaded in a single line, followed by tight, information-dense paragraphs organized by concern (rows, truncation, pagination, counters, regions). It is long but nearly every sentence carries a distinct caveat; only the densest pagination paragraph risks being more than the agent needs up front.

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?

For a 10-parameter catalog tool with an output schema and clean annotations, this covers the edge cases an agent would otherwise get wrong: unknown brands, truncation, misleading pagination, and under-counting counters. Nothing material is missing.

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 description coverage is 100%, so every parameter is already documented in the schema. The prose largely restates output semantics rather than adding parameter syntax or constraints beyond what the schema provides, so the baseline of 3 applies.

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?

States a specific verb+resource ('List tire models of one brand') and immediately enumerates the returned row fields. It is clearly distinguishable from siblings by naming tires_search_advanced and tires_list_sizes as the escape hatches for the cases this tool handles poorly.

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 routes the agent: when the catalog is truncated, 'narrow filters or use tires_search_advanced(brands=[...]) which paginates properly'; when counters.modes reads 0, 'verify with tires_list_sizes, never infer absence.' It also names tires_list_brands and tires_list_regions as slug sources, giving clear when-to-use context.

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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