Skip to main content
Glama
yevhen-kalyna

autoria-mcp

search_used_cars

Search AUTO.RIA used-car listings by brand, model, region, fuel, gearbox, and more. Returns matching ad IDs, count, and page info.

Instructions

Search AUTO.RIA used-car listings (passenger cars) by friendly inputs.

Resolves brand/model/region/city/fuel/gearbox/body names to AUTO.RIA ids for you, then makes a single search call. Returns the total match count, the current page/page_size, and the matching advert ids (used cars only — the OfferOfTheDay promo is filtered out). To get a specific listing's details and its own auto.ria.com URL, pass an id to get_car_details, or set include_details=true to get them inline.

Notes:

  • Mileage is in km (e.g. mileage_to=150000); engine volume is in litres (engine_volume_from=1.9), power in hp (power_hp_from).

  • Engine volume is stored imprecisely upstream (a 2.0 may appear as 1.97), so use a band (e.g. 1.9–2.1) rather than an exact value.

  • model requires brand; city requires region. To target Kyiv city, use region="Київська", city="Київ" (there is no separate Kyiv region). Filter by generation_id/modification_id (from list_generations / list_modifications) to isolate a specific generation or engine/trim.

  • v1 supports a single brand+model block. For multi-brand queries use the raw_search tool.

Example: search_used_cars(brand="Audi", model="A6", body="Універсал", engine_volume_from=1.9, engine_volume_to=2.1, fuel="Дизель", order_by="price_asc").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyNoBody style name, e.g. 'Седан', 'Універсал'.
cityNoCity name; requires `region`, e.g. 'Жмеринка'.
fuelNoFuel type name, e.g. 'Дизель', 'Електро'. 'Гібрид' (or 'hybrid') covers all subtypes (HEV/PHEV/MHEV/REEV) in one call — note this includes MHEV (mild hybrids, essentially ICE); for full/plug-in hybrids pass an exact subtype like 'Гібрид (HEV)' / 'Гібрид (PHEV)'.
pageNo0-based page number.
brandNoBrand/marque name, e.g. 'BMW', 'Toyota'.
modelNoModel name; requires `brand`, e.g. '3 Series'.
regionNoRegion/oblast name, e.g. 'Київська'.
gearboxNoGearbox name, e.g. 'Автомат', 'Ручна / Механіка'.
year_toNoLatest model year, e.g. 2020.
currencyNoPrice currency: USD, EUR, or UAH.USD
order_byNoSort order — pass a name (preferred) or the legacy int: relevance (0), price_asc (2), price_desc (3), year_desc (5, newest), year_asc (6), date_desc (7, newest listing), date_asc (8), mileage_desc (12), mileage_asc (13). The default 'relevance' may surface promoted/closest matches first and bury the cheapest or newest; for an exhaustive or price-ranked sweep pass an explicit sort (e.g. 'price_asc') and page through `count`.relevance
price_toNoMaximum price in `currency`.
page_sizeNoResults per page (1-100).
year_fromNoEarliest model year, e.g. 2015.
mileage_toNoMaximum mileage in km, e.g. 150000.
price_fromNoMinimum price in `currency`.
power_hp_toNoMax engine power in hp (к.с.).
mileage_fromNoMinimum mileage in km, e.g. 50000.
generation_idNoGeneration id(s) from `list_generations`. Pass several to span facelifts of one generation (e.g. C7, C7 FL).
power_hp_fromNoMin engine power in hp (к.с.).
include_detailsNoIf true, also fetch each listing on this page (compact details, aligned to `ids`) in one call — removes the usual follow-up `get_car_details_batch`. Costs ~`page_size` extra upstream calls, so keep `page_size` small; capped at page_size<=50.
modification_idNoModification id(s) from `list_modifications`.
engine_volume_toNoMax engine volume in litres, e.g. 2.1.
engine_volume_fromNoMin engine volume in litres, e.g. 1.9.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idsNo
pageNo
countNo
detailsNo
page_sizeNo
Behavior4/5

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

No annotations provided, so the description carries the full burden. It discloses return values (count, page, page_size, ids), that OfferOfTheDay promo is filtered out, that engine volume is imprecise, and that include_details incurs extra upstream calls. It lacks some details like rate limits or auth, but for a search tool this is sufficient.

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 well-structured with a brief overview, then bulleted notes, and an example. Every sentence adds value; there is no fluff despite the large parameter count. It is front-loaded with the core purpose.

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 24 parameters (many optional, with dependencies), no annotations, and an output schema, the description covers all essential aspects: what the tool returns, how to use parameters effectively, limitations, and pointers to sibling tools. It is complete for an AI agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds substantial value: explains name resolution to IDs, units (km, litres, hp), imprecision advice, dependency constraints, sort order details, and includes an example. This goes far beyond the schema descriptions.

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 'Search AUTO.RIA used-car listings (passenger cars) by friendly inputs.' It specifies the verb (search), resource (used cars), and scope (AUTO.RIA, passenger cars). It distinguishes from siblings like raw_search and get_car_details, making the tool's unique role evident.

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 provides when-to-use and when-not: 'For multi-brand queries use the raw_search tool.' It also explains dependencies (model requires brand, city requires region) and offers alternatives for getting details (include_details or get_car_details). This gives comprehensive guidance.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/yevhen-kalyna/autoria-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server