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Search vehicle listings

search_vehicles
Read-onlyIdempotent

Search 1.8M+ Brazilian vehicle listings aggregated from 96 platforms (OLX, Webmotors, Napista, etc.). Returns listings with price, year, mileage, location, source platform, the original ad URL, FIPE reference price and a deal_score (% vs FIPE; negative means priced below FIPE).

Tips for best results:
- Free-text `query` matches the listing TITLE only (e.g. "Onix LTZ turbo").
- For exact brand/model/version filtering, resolve names to ids first with
  list_brands / list_models / list_versions and pass brand_id/model_id/
  version_id — it is faster and more accurate than the text fields.
- `location` is a 2-letter UF (e.g. "SP") or a city name.
- `sort_by="newest"` (default) lists the most recently LISTED cars first —
  the date a listing first appeared on its source, not when we last saw it.
- `sort_by="best_deal"` ranks by lowest price-to-FIPE ratio.
- `per_page` is capped at 50. Use `page` (1-20) to paginate; past the first
  20 pages, narrow the search with filters. `total_count` and
  `total_pages` are returned.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
brandNo
modelNo
queryNo
sourceNo
sort_byNonewest
versionNo
brand_idNo
categoryNoCars
locationNo
model_idNo
per_pageNo
year_maxNo
year_minNo
fuel_typeNo
price_maxNo
price_minNo
body_styleNo
version_idNo
mileage_maxNo
mileage_minNo
transmissionNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already mark read-only and idempotent, but the description adds rich behavioral context: deal_score semantics, sort_by='newest' meaning (listed date, not last seen), per_page cap, and total_count/total_pages in response. No contradiction with readOnlyHint.

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?

Well-structured with a concise intro followed by bulleted tips. Every sentence adds value: the intro defines scope and output, tips clarify semantics and boundaries. No redundant fluff, front-loaded with the core purpose.

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 complex tool with 22 optional parameters and no output schema, the description effectively communicates the response shape, pagination behavior, and deal_score interpretation. It covers the most impactful parameters but omits detailed explanations for many filters, which is acceptable given the breadth.

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 has zero descriptions, so the description carries the burden. It explains several key parameters: query (title match), location (UF or city), sort_by options, per_page cap, and page usage. However, many filter parameters (price, year, transmission, fuel_type, etc.) are left unexplained, and the description doesn't cover all 22 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 explicitly states it searches 1.8M+ Brazilian listings aggregated from 96 platforms and enumerates the returned fields (price, year, mileage, location, source, URL, FIPE price, deal_score). This clearly differentiates it from siblings like get_vehicle_by_id (single vehicle) and list_brands (metadata).

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?

Provides actionable usage tips: when to use free-text query vs resolved IDs, explains location format, sort options, and pagination limits with advice to narrow past page 20. While it doesn't explicitly contrast with all sibling tools, the guidance on pre-resolving with list_* tools implies the workflow.

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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