carsearch-mcp

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search_live_marketplace

Run a fresh Canadian used-car marketplace search, save all results with history, and return matching listings from the local database, including previously seen ones. Get up-to-the-minute listings.

Instructions

Run a fresh marketplace search (stores everything with history), then return matching listings from the local DB including ones seen earlier. Use for up-to-the-minute results.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`make`	No
`sort`	No	newest \| price_asc \| price_desc \| mileage_asc \| distance \| relevance	newest
`limit`	No	Page size (max 200).
`model`	No
`keywords`	No
`location`	No	Free-text centre for radius search: city ('Vaughan'), 'City, PROV', or postal code ('M5V 3L9').
`provider`	No	autotrader (others: cargurus/kijiji/facebook are placeholders)	autotrader
`province`	No	Single province code; without `location` the crawl uses the province centroid + covering radius, and results are filtered exactly to the province locally
`year_max`	No
`year_min`	No
`fuel_type`	No
`max_pages`	No	Result pages to fetch (20 listings each). Politeness: ~1.5s per page.
`price_max`	No
`price_min`	No
`radius_km`	No	Radius km (marketplace supports up to ~1000)
`body_style`	No
`drivetrain`	No
`mileage_max`	No
`seller_type`	No
`transmission`	No
`fetch_details`	No	Fetch detail pages for NEW listings (VIN, coordinates, drivetrain, features; ~1.5s each)

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 17, 2026

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses a key side effect: 'stores everything with history', and clarifies that results are read from the local DB, not just live. This goes beyond a simple search description and informs the agent about persistence behavior, though it omits rate limits or other operational details present in the schema.

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 two sentences, front-loads the essential behavior (fresh search, storage, local DB return), and avoids waste. Each sentence contributes a distinct, useful fact, making it highly efficient for its size.

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

Completeness2/5

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

Despite an output schema covering return values, the tool has 21 parameters with low schema coverage and no annotations. The description is too thin to be contextually complete: it does not explain parameter interplay, which filters are supported, or the implications of 'stores everything with history' for the agent's decision-making.

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

Parameters2/5

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

Schema description coverage is only 38%, so the description must compensate for the many undocumented parameters. It does not mention or explain any of the 21 parameters (make, model, sort, price, etc.). The phrase 'matching listings' gives no concrete guidance on how filters map to behavior, leaving agents with inadequate parameter semantics.

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 states a specific verb 'Run a fresh marketplace search' and resource 'local DB', and distinctly signals it returns 'matching listings from the local DB including ones seen earlier'. This differentiates it from sibling tools like search_cars and get_new_listings by emphasizing freshness, persistence, and historical inclusion.

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?

The description gives a clear usage context: 'Use for up-to-the-minute results.' It does not explicitly name alternatives or state when not to use it, but the freshness cue implies when this tool is preferred over other search tools, which is adequate without exclusions.

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