eliac7/cargr-api

Car.gr API

Unofficial FastAPI-based API & MCP Server for scraping and accessing car listings from car.gr, the largest Greek car marketplace.

Features

• 🔍 Search API: Search using native car.gr filters
• 📋 Car Details: Get detailed specifications, pricing, and seller info
• 🚗 Comprehensive Data: Engine specs, Euro class, KTEO, and more
• 🛡️ Stealth Requests: Retries and stealth techniques to reduce blocking
• ⚡ FastAPI: High-performance API with auto-generated Swagger docs

Installation

Prerequisites

• Python 3.13+
• uv (recommended) or pip

Setup

1. Clone the repository

git clone <repository-url>
cd cargr-api

2. Install dependencies

Using uv (recommended):

uv pip install -e .

Using pip:

pip install -e .

Docker

Build and run the containerized application:

# Build image
docker build -t cargr-api .

# Run container
docker run -p 8000:8000 cargr-api

The API will be available at:

http://localhost:8000

Usage

Running the Server

uvicorn main:app --reload

or, if you run the main.py directly (if provided):

python main.py

API Documentation

Once running, access:

• Swagger UI: http://127.0.0.1:8000/docs
• ReDoc: http://127.0.0.1:8000/redoc

MCP Server Integration

This API includes a Model Context Protocol (MCP) server, allowing AI assistants (like Claude Desktop, Cursor, etc.) to directly search for cars and retrieve details using natural language.

Connecting via SSE (Recommended)

To use this API with an MCP client, add the following configuration to your client's settings (e.g., claude_desktop_config.json):

{
  "mcpServers": {
    "cargr-mcp": {
      "url": "http://127.0.0.1:8000/mcp"
    }
  }
}

Once connected, the AI will have access to the following tools:

• search_car_listings: Search for cars using filters (requires fetching filter IDs first).
• get_car_details_by_id: Get full details for a specific car listing.
• get_available_filters: Retrieve valid IDs for makes, models, and other categorical fields.

Important Note for AI Usage

When using the search tool, the AI must first use get_available_filters to look up the correct IDs for parameters like make, model, fuel_type, etc. Guessing IDs will likely result in empty or incorrect results.

API Endpoints

1. Get Car Details

Retrieve full details for a specific classified listing.

GET /api/car/{car_id}

Example:

curl http://127.0.0.1:8000/api/car/48949544

Response:

{
  "car_id": "48949544",
  "title": "Fiat 2020 Fiorino Professional",
  "price": 10200.0,
  "make": "Fiat",
  "model": "Fiorino",
  "km": 97000.0,
  "engine": 1300,
  "bhp": 95,
  "fueltype": "Diesel",
  "transmission": "Manual",
  "euro_class": "Euro 6",
  "city": "Άγιος Δημήτριος, Ν. Αττικής",
  "postal_code": "17341",
  "is_dealer": true,
  "seller_name": "Tolias Edition",
  "url": "https://www.car.gr/classifieds/vans/view/48949544",
  "images": ["https://.../1.jpg", "https://.../2.jpg"],
  "features": ["air conditioning", "abs"],
  "views": 120,
  "modified_at": "2025-11-10T12:34:56Z",
  "scraped_at": "2025-12-03T15:00:00Z"
}

2. Search Cars

Search for listings. This endpoint accepts query parameters that map to car.gr's search filters.

GET /api/search?{query_params}

Example:

curl "http://127.0.0.1:8000/api/search?make=13302&fuel_type=2&price-to=12000"

Common Parameters (examples):

Response:

Returns an array of Car objects matching the query, along with pagination metadata.

Data Model

The API returns a comprehensive Car object with fields such as:

• Identification
  • car_id, title, url
• Categorization
  • make, model, category
• Specifications
  • release_date, engine (cc), km, bhp, color, fueltype, transmission, doors, seats
• Regulatory
  • euro_class, road_tax, kteo_expiry (MOT)
• Location
  • city, region, postal_code
• Seller
  • is_dealer (bool), seller_name, description, views, modified_at
• Media
  • images (array of image URLs)
• Features
  • features (array of strings)
• Metadata
  • scraped_at (ISO 8601 timestamp)

Architecture

• main.py: FastAPI application with route handlers
• models.py: Pydantic models for request/response validation
• parser.py: Web scraping logic using BeautifulSoup and stealth requests
• services/: Modular scraping, caching, and rate-limiting helpers
• docker/: Dockerfile and helper scripts

Parsing strategies

1. JSON-LD structured data (primary, when available)
2. HTML parsing with BeautifulSoup and targeted selectors
3. Regex fallbacks for specific embedded values
4. Tailwind grid/table parsing for complex layouts

Rate Limiting & Ethics

This project includes defensive measures to avoid overloading car.gr:

• Randomized delays between requests
• Exponential backoff and retry logic
• Rate-limit detection and respectful pausing
• Optional caching layer to reduce repeated requests

Please use responsibly, respect car.gr's robots.txt, and read their Terms of Service before scraping. Avoid high-frequency scraping or commercial use without permission.

Dependencies

Key Python packages used:

• fastapi
• uvicorn
• beautifulsoup4
• stealth-requests (or similar stealth HTTP client)
• pydantic
• requests
• optional: httpx, tenacity, cachetools

Install via the provided setup.py/pyproject.toml or the editable install command above.

Examples

Search for a Toyota petrol under €20,000:

curl "http://127.0.0.1:8000/api/search?make=13301&price-to=20000&fuel_type=1"

Get details for a specific listing:

curl http://127.0.0.1:8000/api/car/48949544

License & Disclaimer

This project is provided for educational purposes only. By using this software you agree to:

• Respect car.gr's robots.txt and Terms of Service
• Not use this tool for abusive, high-frequency, or commercial scraping without explicit permission
• Understand that the authors are not responsible for misuse or any legal consequences that may arise

No warranty, use at your own risk.

Contributing

Contributions, bug reports, and pull requests are welcome. Please open issues for feature requests or problems, and follow the project's code style and testing guidelines.

Contact

For questions or help, open an issue on the GitHub repository.