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OmniaAuto — Canadian Vehicle Listings

Market Stats

market_stats
Read-only

COST: 1 credit per call.

Returns vehicle counts and average and median price and mileage for a slice of Canadian listings, with optional group_by breakdowns.

Input Schema

NameRequiredDescriptionDefault
makeNoMake, e.g. Toyota; exact match, any case
nearNoCentre of a radius search: city, postal code or lat,lng (e.g. 53.54,-113.49); radius_km sets its size
sortNon = largest groups first (default); sold_30d = most sold in 30 days, needs group_by and condition Used or New
modelNoModel, e.g. RAV4; exact match, any case
group_byNoUp to 2 of city, trim, year, condition, model (largest 25 groups, rest as Other); city needs province or near, trim needs make and model
provinceNo2-letter province code, e.g. AB, ON, QC; empty = all Canada
year_maxNoLatest model year, e.g. 2024; 0 = any
year_minNoEarliest model year, e.g. 2020; 0 = any
body_typeNoSUV, Sedan, Pickup, Hatchback, Minivan, Coupe, Van, Convertible or Wagon; empty = any
conditionNoNew, Used, Certified or Demo; empty = any
radius_kmNoRadius around near, in km (default 25, max 500); requires near
seller_typeNodealer (default) or private: private-seller listings, a separate segment (seller Private, city and province only, no link or VIN)

Output Schema

NameRequiredDescriptionDefault
geoNoResolved near anchor and radius_km, on a radius search
noteNo
unitNoAlways vehicle
errorNoSet on a refusal, e.g. out_of_credits, not_found; result fields are then absent
detailNo
groupsNoOne object per group, with n and price figures
allowedNoAccepted values, on an invalid_* error
pricingNo
summaryNon, unique_vins, avg_price, median_price, min_price, max_price, avg_mileage, priced
group_byNoBreakdown dimensions applied
by_provinceNo
by_conditionNo
by_certificationNo
credits_remainingNoCredits left in the current period

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / seller_type
      Added value: +{
      +  "default": "",
      +  "description": "dealer (default) or private: private-seller listings, a separate segment (seller Private, city and province only, no link or VIN)",
      +  "title": "Seller Type",
      +  "type": "string"
      +}
  2. Changed12 schema fields changed
    • changedInput schema / properties / body_type / description
      Previous value: -"e.g. SUV, Sedan, Pickup"New value: +"SUV, Sedan, Pickup, Hatchback, Minivan, Coupe, Van, Convertible or Wagon; empty = any"
    • changedInput schema / properties / condition / description
      Previous value: -"New, Used, Certified or Demo"New value: +"New, Used, Certified or Demo; empty = any"
    • changedInput schema / properties / group_by / description
      Previous value: -"Breakdown: city, trim, year, condition or model"New value: +"Up to 2 of city, trim, year, condition, model (largest 25 groups, rest as Other); city needs province or near, trim needs make and model"
    • changedInput schema / properties / make / description
      Previous value: -"Make, e.g. Toyota"New value: +"Make, e.g. Toyota; exact match, any case"
    • changedInput schema / properties / model / description
      Previous value: -"Model, e.g. RAV4"New value: +"Model, e.g. RAV4; exact match, any case"
    • changedInput schema / properties / near / description
      Previous value: -"City, postal code or lat,lng"New value: +"Centre of a radius search: city, postal code or lat,lng (e.g. 53.54,-113.49); radius_km sets its size"
    • changedInput schema / properties / province / description
      Previous value: -"Province, e.g. AB"New value: +"2-letter province code, e.g. AB, ON, QC; empty = all Canada"
    • changedInput schema / properties / radius_km / description
      Previous value: -"Radius around near, km (default 25, max 500)"New value: +"Radius around near, in km (default 25, max 500); requires near"
    • changedInput schema / properties / sort / description
      Previous value: -"n (default) or sold_30d"New value: +"n = largest groups first (default); sold_30d = most sold in 30 days, needs group_by and condition Used or New"
    • changedInput schema / properties / year_max / description
      Previous value: -"Latest model year"New value: +"Latest model year, e.g. 2024; 0 = any"
    • changedInput schema / properties / year_min / description
      Previous value: -"Earliest model year"New value: +"Earliest model year, e.g. 2020; 0 = any"
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "additionalProperties": true,
      +  "properties": {
      +    "allowed": {
      +      "description": "Accepted values, on an invalid_* error",
      +      "items": {},
      +      "type": [
      +        "array",
      +        "null"
      +      ]
      +    },
      +    "by_certification": {
      +      "additionalProperties": true,
      +      "type": [
      +        "object",
      +        "null"
      +      ]
      +    },
      +    "by_condition": {
      +      "items": {},
      +      "type": [
      +        "array",
      +        "null"
      +      ]
      +    },
      +    "by_province": {
      +      "items": {},
      +      "type": [
      +        "array",
      +        "null"
      +      ]
      +    },
      +    "credits_remaining": {
      +      "description": "Credits left in the current period",
      +      "type": [
      +        "integer",
      +        "null"
      +      ]
      +    },
      +    "detail": {
      +      "type": [
      +        "string",
      +        "null"
      +      ]
      +    },
      +    "error": {
      +      "description": "Set on a refusal, e.g. out_of_credits, not_found; result fields are then absent",
      +      "type": [
      +        "string",
      +        "null"
      +      ]
      +    },
      +    "geo": {
      +      "additionalProperties": true,
      +      "description": "Resolved near anchor and radius_km, on a radius search",
      +      "type": [
      +        "object",
      +        "null"
      +      ]
      +    },
      +    "group_by": {
      +      "description": "Breakdown dimensions applied",
      +      "items": {},
      +      "type": [
      +        "array",
      +        "null"
      +      ]
      +    },
      +    "groups": {
      +      "description": "One object per group, with n and price figures",
      +      "items": {},
      +      "type": [
      +        "array",
      +        "null"
      +      ]
      +    },
      +    "note": {
      +      "type": [
      +        "string",
      +        "null"
      +      ]
      +    },
      +    "pricing": {
      +      "additionalProperties": true,
      +      "type": [
      +        "object",
      +        "null"
      +      ]
      +    },
      +    "summary": {
      +      "additionalProperties": true,
      +      "description": "n, unique_vins, avg_price, median_price, min_price, max_price, avg_mileage, priced",
      +      "type": [
      +        "object",
      +        "null"
      +      ]
      +    },
      +    "unit": {
      +      "description": "Always vehicle",
      +      "type": [
      +        "string",
      +        "null"
      +      ]
      +    }
      +  },
      +  "type": "object"
      +}
  3. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely new context the annotations lack: the cost of 1 credit per call, which is essential for an agent managing a budget. It stops short of describing aggregation edge cases (e.g. thin data or the separate private-seller segment).

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?

Two short sentences, zero filler, with the cost warning front-loaded ahead of the functional summary. Every clause carries information the agent needs.

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?

With 12 optional parameters, an output schema and full annotation coverage, the definition supplies what the structured fields cannot: cost and the aggregate nature of the result. The main remaining gap is sibling disambiguation against valuation and search_listings.

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%, with each of the 12 parameters documented in detail including group_by constraints and sort/condition dependencies. The description only echoes 'group_by breakdowns' and adds no format or syntax detail beyond the schema, so the baseline 3 applies.

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 names a specific output (vehicle counts, average and median price and mileage) over a defined scope (a slice of Canadian listings) with optional group_by breakdowns. An agent can tell it produces aggregates rather than individual listings. It does not explicitly distinguish itself from siblings such as valuation or search_listings.

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?

Usage is only implied: returning counts and price/mileage aggregates signals market-analysis use rather than listing retrieval, and group_by implies segment comparison. There is no explicit statement of when to prefer this over valuation or coverage, and no exclusions or prerequisites.

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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