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yevhen-kalyna

autoria-mcp

get_average_price

Calculate average market price for a used car in Ukraine using AI estimates and comparable listings. Input a VIN/plate/advert ID or specify car parameters to get price insights and sample statistics.

Instructions

Paid. AI average price + comparable listings (point-in-time).

Two modes: pass omni_id (VIN/plate/advert id), OR car parameters by name (brand+model required, plus at least one more filter such as year_from/mileage_to/fuel/engine_volume_from). Names are resolved to ids for you; generation_id/modification_id are optional raw ids. Requires AUTORIA_USER_ID. period ∈ {30, 90, 180, 365}.

The headline avg_price_usd/avg_price_uah is AUTO.RIA's own model-level AI estimate — it is only weakly sensitive to engine_volume/modification, so do NOT read it as an engine-precise fair value. For that, prefer cohort_estimate_usd (the median of the comparable listings), keeping in mind the sample is small. To judge reliability, the response also returns sample_count and the sample's own sample_min_usd/sample_median_usd/ sample_max_usd, plus a price_consistency flag that is avg_below_sample/ avg_above_sample when the headline falls outside its own comparables. status is no_data / insufficient_sample / ok — it is insufficient_sample when fewer than 5 comps are in-cohort or when a tight cohort was requested yet the headline ignored it. Comps are flagged in_cohort and cohort_estimate_usd uses only those (in_cohort_count reports how many). cohort echoes the resolved filters. quota is a LOCAL, advisory, warn-only counter (it can exceed its limit and never blocks) — not AUTO.RIA's enforced budget, so don't hard-gate on it.

Note: facelifts are distinct generation_ids and this endpoint takes a single one — call once per generation to price a whole family.

Example: get_average_price(brand="Peugeot", model="308", fuel="Дизель", engine_volume_from=1.9, engine_volume_to=2.1, year_from=2014, period=365).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyNoBody style name.
fuelNoFuel type name.
brandNoBrand name.
colorNoColour name.
driveNoDrive type name.
modelNoModel name.
periodNoPeriod in days; one of 30, 90, 180, 365.
regionNoRegion name.
gearboxNoGearbox name.
omni_idNoVIN, plate number, or advert id. Skips by-params mode.
year_toNoLatest year.
year_fromNoEarliest year.
mileage_toNoMax mileage in km.
mileage_fromNoMin mileage in km.
generation_idNoRaw generation id (improves accuracy). One only — facelifts are separate ids, so call once per generation to span them.
include_samplesNoInclude the `similar_cars` comparable listings. Set False for a lighter stats-only response (sample size + spread are still returned).
modification_idNoRaw modification id (improves accuracy).
engine_volume_toNoMax engine volume in litres, e.g. 2.1. Narrows the comparable sample, NOT the model-level headline estimate.
engine_volume_fromNoMin engine volume in litres, e.g. 1.9. Narrows the comparable sample, NOT the model-level headline estimate.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
quotaNo
cohortNo
periodNo
statusNook
sample_countNo
similar_carsNo
avg_price_uahNo
avg_price_usdNo
sample_max_usdNo
sample_min_usdNo
statistic_dataNo
in_cohort_countNo
price_consistencyNo
sample_median_usdNo
cohort_estimate_usdNo
Behavior5/5

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

With no annotations, the description thoroughly discloses behavior: the headline estimate is model-level AI (weakly sensitive to engine/modification), the cohort estimate is the median of comparables (with sample size caveats), status meanings, price_consistency flag, quota is advisory, and facelifts are separate generation_ids. This is exceptional transparency.

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 detailed but well-structured with bolded summary, bullet lists, and an example. It is slightly long for the complexity, but every sentence adds value. Minor improvements could trim redundant phrases.

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 19 parameters, 100% schema coverage, and an output schema, the description covers all key aspects: modes, requirements, caveats, status codes, quota, and an example. It is comprehensive and leaves little ambiguity.

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%, but the description adds critical context beyond the schema: e.g., engine_volume_from/to narrow the comparable sample not the headline estimate, generation_id is one-only and facelifts are separate IDs, and the two-mode interplay. This significantly aids correct usage.

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 clearly states the tool computes AI average price and comparable listings in point-in-time modes (omni_id or by parameters). It specifies the resource and action, but does not explicitly differentiate from sibling tool get_average_price_over_periods, which is a minor gap.

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 clear guidance on two usage modes, required parameters (brand+model+at least one filter), valid period values, and an example. However, it does not explicitly state when to avoid this tool or mention alternatives like get_average_price_over_periods.

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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