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JavaScript
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datasets_vehicle_listings_facets

Retrieve facet counts for vehicle listings by source, make, model, and more. Filter by price, year, mileage, and other attributes to analyze market data.

Instructions

Facet vehicle listings dataset. Returns terms aggregation counts for the vehicle listings dataset. Facet enum: source, make, model, trim, body_style, transmission, drive_type, fuel_type, seller_type, state, run_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over make, model and trim, max 256 characters
vinNoExact VIN filter
makeNoExact make filter, max 128 characters
trimNoExact trim filter, max 128 characters
facetYesFacet enum: source, make, model, trim, body_style, transmission, drive_type, fuel_type, seller_type, state, run_id
modelNoExact model filter, max 128 characters
stateNoExact US state abbreviation filter, max 8 characters
run_idNoExact crawl run-id filter, max 128 characters
sourceNoExact source marketplace filter: carmax, autotrader, carsdotcom
max_yearNoMaximum model year
min_yearNoMinimum model year
fuel_typeNoExact fuel type filter, max 128 characters
max_priceNoMaximum price in US dollars
min_priceNoMinimum price in US dollars
body_styleNoExact body style filter, max 128 characters
drive_typeNoExact drivetrain filter, max 128 characters
max_mileageNoMaximum odometer mileage
seller_typeNoExact seller type filter: retailer, dealer, private
transmissionNoExact transmission filter, max 128 characters
is_price_reducedNoFilter for listings currently marked down from a previous price

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "source",
+  "make",
+  "model",
+  "trim",
+  "body_style",
+  "transmission",
+  "drive_type",
+  "fuel_type",
+  "seller_type",
+  "state",
+  "run_id"
+]
    • addedInput schema / properties / seller_type / enum
      Added value: +[
+  "retailer",
+  "dealer",
+  "private"
+]
    • addedInput schema / properties / source / enum
      Added value: +[
+  "carmax",
+  "autotrader",
+  "carsdotcom"
+]
  2. Addedv1.16.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose the key behavioral trait—'returns terms aggregation counts'—but says nothing about read-only behavior, limits, pagination, or response structure. This is a baseline disclosure, not a rich one.

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?

The description is short and front-loaded with the core purpose. The only minor issue is the redundancy of 'vehicle listings dataset' appearing twice, but there is no padding or irrelevant detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (20 parameters, no output schema, no annotations), the description is thin. It establishes the basic purpose but doesn't explain how filters interact with facet counts, the shape of the response buckets, or any operational limits. Basic usage is inferable, but more context would be needed for confident invocation.

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?

All 20 parameters are already documented in the input schema (100% coverage), so the schema carries the parameter semantics. The description merely restates the facet enum that is already in the schema, adding no new meaning beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states that the tool facets the vehicle listings dataset and returns terms aggregation counts, and it enumerates the facet field options. This clearly identifies the operation and resource, and the aggregation nature distinguishes it from item/search siblings, though it doesn't explicitly name those siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit statement of when to use this tool versus alternatives like datasets_vehicle_listings_search or datasets_vehicle_listings_item. The purpose implies it is for getting facet counts rather than listings, but the agent must infer that from the sibling set and the word 'facets'.

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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