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Car Deals Search MCP

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Car Deals Search MCP

Search used car listings from Cars.com, Autotrader, and KBB with AI assistants

An MCP (Model Context Protocol) server that aggregates and searches car listings from multiple sources. Scrapes listings in parallel, extracts price, mileage, dealer info, and applies optional CARFAX-style filters (1-owner, no accidents, personal use).

License: MIT

🚀 Quick Start

Prerequisites

  • Node.js (v16 or higher)

  • Chrome/Chromium browser installed (required by Puppeteer)

    • If Chrome is not in the default location, set PUPPETEER_EXECUTABLE_PATH environment variable to point to your Chrome/Chromium binary

Installation

# Clone the repository
git clone https://github.com/SiddarthaKoppaka/car_deals_search_mcp.git
cd car_deals_search_mcp

# Install dependencies (includes Puppeteer)
npm install

Using with MCP Clients

Configure your MCP client (Claude Desktop, VS Code, GitHub Copilot, etc.) to use this server:

For Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "car-deals": {
      "command": "node",
      "args": ["/absolute/path/to/car_deals_search_mcp/src/server.js"]
    }
  }
}

For other MCP clients, refer to their documentation and use:

  • Command: node

  • Args: ["<absolute-path-to-repo>/src/server.js"]

Testing Standalone

# Run the test command
npm test

# Or test manually with a specific search
node -e "
const { scrapeCarscom } = require('./src/scraper.js');
scrapeCarscom({
  make: 'Toyota',
  model: 'Camry',
  oneOwner: true,
  noAccidents: true,
  personalUse: true
}, 5).then(listings => listings.forEach(l => console.log(l.format())));
"

Related MCP server: MarketCheck MCP Apps

✨ Features

  • Multi-source aggregation: Search Cars.com, Autotrader, and KBB simultaneously

  • Smart filtering: CARFAX-style filters (1-Owner, No Accidents, Personal Use)

  • Deal ratings: Heuristic-based deal quality assessment

  • Parallel scraping: Fast concurrent queries across sources

  • Stealth mode: Puppeteer with anti-bot detection techniques

📊 Supported Sources

Source

Price

Mileage

Deal Rating

Dealer Info

CARFAX Filters

Cars.com

✅

✅

✅

✅

✅

Autotrader

✅

✅

⚠️ Limited

✅

⚠️ Limited

KBB

✅

✅

✅

⚠️ Limited

⚠️ Limited

🔧 MCP Tool: search_car_deals

Parameters

Parameter

Type

Required

Description

make

string

✅

Car manufacturer (e.g., "Toyota", "Honda")

model

string

✅

Car model (e.g., "Camry", "Accord")

zip

string

❌

ZIP code for local search (default: "90210")

yearMin

integer

❌

Minimum model year

yearMax

integer

❌

Maximum model year

priceMax

integer

❌

Maximum price in USD

mileageMax

integer

❌

Maximum mileage

maxResults

integer

❌

Max results per source (default: 10)

sources

array

❌

Sources to query: ["cars.com","autotrader","kbb"] (default: all)

oneOwner

boolean

❌

Filter for CARFAX 1-owner vehicles only

noAccidents

boolean

❌

Filter for no accidents reported

personalUse

boolean

❌

Filter for personal use only (not rental/fleet)

Example Response

🚗 2021 Toyota Camry XSE
   💰 Price: $23,491
   📏 Mileage: 52,649 mi
   ⭐ Deal Rating: Good Deal
   🏆 CARFAX: 1-Owner | No Accidents | Personal Use
   🏪 Dealer: Valencia BMW
   🌐 Source: Cars.com
   🔗 https://www.cars.com/vehicledetail/...

🛠️ Technical Details

  • Scraping: Puppeteer (headless Chromium) with stealth plugin to bypass bot detection

  • Concurrency: Parallel scraper workers for simultaneous multi-source queries

  • Protocol: Implements MCP (Model Context Protocol) for AI assistant integration

  • Data extraction: Source-specific parsers normalize listings into a common schema

Chrome/Chromium Requirement

This project uses Puppeteer, which requires Chrome or Chromium to be installed:

  • macOS: Chrome is typically at /Applications/Google Chrome.app/Contents/MacOS/Google Chrome

  • Linux: Usually auto-detected by Puppeteer or at /usr/bin/chromium-browser

  • Windows: Typically at C:\Program Files\Google\Chrome\Application\chrome.exe

If Puppeteer cannot find your browser, set the environment variable:

export PUPPETEER_EXECUTABLE_PATH="/path/to/chrome"

🧪 Development & Testing

# Run tests
npm test

# Test individual scrapers
node src/scraper.js

# View code structure
ls -la src/

🤝 Contributing

Contributions are welcome! Please follow this workflow:

  1. Fork the repository

  2. Create a feature branch (git checkout -b feature/amazing-feature)

  3. Add tests for new functionality

  4. Commit your changes (git commit -m 'Add amazing feature')

  5. Push to the branch (git push origin feature/amazing-feature)

  6. Open a Pull Request

Please include test coverage for scraping/parsing changes to avoid regressions when source sites update.

📄 License

MIT License - see LICENSE file for details

🔗 Links

Available Tools

1 tool
search_car_dealsA

Search for car deals across multiple sources (Cars.com, Autotrader, KBB). Returns listings with prices, mileage, deal ratings, and links.

ParametersJSON Schema
NameRequiredDescriptionDefault
zipNoZIP code for location-based search (default: 90210)
makeYesCar manufacturer (e.g., Toyota, Honda, Ford)
modelYesCar model (e.g., Camry, Civic, F-150)
sourcesNoSources to search: "cars.com", "autotrader", "kbb". Default: all
yearMaxNoMaximum model year
yearMinNoMinimum model year
oneOwnerNoFilter for CARFAX 1-Owner vehicles only
priceMaxNoMaximum price in dollars
maxResultsNoMaximum results per source (default: 10)
mileageMaxNoMaximum mileage
noAccidentsNoFilter for vehicles with no accidents or damage reported
personalUseNoFilter for vehicles used for personal use only (not rental/fleet)

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool searches across multiple sources and returns listings with specific fields. However, it does not mention any limitations, rate limits, authentication requirements, or what happens if no results are found. It does not contradict anything, but it lacks depth in behavioral disclosure beyond the basic function.

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 a single, concise sentence that front-loads the primary action and result. Every part is informative with no waste. It efficiently communicates the tool's purpose without unnecessary elaboration.

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?

Despite 12 parameters and no output schema, the description provides enough context by mentioning the return fields (prices, mileage, deal ratings, links). It does not explain ordering, pagination, or error handling, but the schema covers parameter semantics. Given the tool's complexity, it is reasonably complete, though slightly more detail on result behavior would justify a 5.

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?

The schema provides 100% coverage with descriptions for every parameter. The tool description does not add additional parameter-specific semantics beyond what the schema already states, such as the use of 'maxResults' or 'oneOwner'. Per the baseline rule, since schema coverage is high, a score of 3 is appropriate.

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 ('Search'), a specific resource ('car deals'), and names the sources (Cars.com, Autotrader, KBB) and return types (prices, mileage, deal ratings, links). It is clear and distinct, though there are no sibling tools to differentiate from. The purpose is unambiguous.

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 clearly implies usage: search for car deals using the provided filters. Since no sibling tools are listed, there is no need to specify alternatives or exclusions. The context is clear, but it does not explicitly state when not to use or what alternatives exist. Given the absence of siblings, this is adequate.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedsearch_car_deals
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.1/5.0

Scored across 1 tool

Disambiguation5/5

With only a single tool, there is no potential for confusion or misselection. The tool's purpose is clear and unambiguous within the server.

Naming Consistency5/5

The single tool name 'search_car_deals' follows a consistent verb_noun convention, making it predictable and readable. No other tools exist to create inconsistency.

Tool Count3/5

The server has only one tool, which is borderline thin. While the narrow scope of 'car deals search' might justify this, users might expect additional related tools (e.g., filtering, saving deals) to make the server feel more complete.

Completeness4/5

The tool covers the primary search functionality across multiple sources, returning comprehensive listing data. Minor gaps exist such as lack of detailed deal views or comparison tools, but the core use case is well-served.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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