How to scrape logistics and supply chain data from Mercado Livre using the Minexa.ai extension
- Minexa.ai
- Jul 2
- 4 min read
Fleet procurement teams and logistics analysts working in Brazil regularly need structured vehicle specification data to compare configurations, validate supplier quotes, and build internal reference datasets. Mercado Livre publishes detailed catalogue pages for commercial vehicles, including full technical comparison tables per trim, but that data sits inside a web page rather than a spreadsheet. Getting it out manually, row by row, is slow and error-prone at any meaningful scale.
This walkthrough shows how to extract that specification data from the Mercado Livre Renault Master Furgão 2025 catalogue page using Minexa.ai, a Chrome extension that detects, extracts, and exports structured data from any web page without requiring any code.
Watch the full tutorial first
The video below covers the complete extraction from start to export. It is worth watching before going through the step-by-step screenshots below.
Step 1: Open Minexa.ai and navigate to the catalogue page
Start by opening the Minexa.ai extension from your Chrome toolbar. The home screen confirms the extension is active and ready to detect data on whatever page you navigate to next.
Navigate to the Mercado Livre Renault Master Furgão 2025 catalogue URL. The page loads the full specification comparison table, which is exactly the structured data you want to capture.
Step 2: Confirm the detected page
Once the page is loaded, the extension popup appears with an option to confirm you are on the right page. This tells Minexa.ai to begin its automatic detection of the data structure, including all repeating rows in the specification table and any pagination present on the page.
Step 3: Review pagination detection
Minexa.ai scans the page for pagination controls and displays what it found. For this catalogue page, it identifies whether a next page button or other navigation is present. You review the result and click Continue to proceed. Pagination across multiple catalogue pages is handled automatically from this point forward.
Step 4: Choose list or list with detail pages
At this stage you decide the depth of extraction. For the Mercado Livre vehicle catalogue, the comparison table on the list page already contains the full specification data per trim. You can choose to extract the single list, or instruct Minexa.ai to also follow each linked detail page for additional information per variant.
Step 5: Select simple or advanced scraping mode
You then choose between simple scraping mode, which extracts the data as detected, and advanced mode, which lets you define custom interaction steps before extraction runs. For a standard specification table like this one, simple mode is sufficient.
Step 6: Highlight the data container
Minexa.ai automatically highlights the full container holding the specification rows. You confirm the selection and click to create the scraper. The extension locks onto the repeating table structure and identifies every data point within it.
Step 7: Review extracted data points
After the scraper is created, you see all extracted columns with navigation arrows to browse through each data point. For this Mercado Livre catalogue page, the extracted fields include the specification label, values per trim variant, the CSS class per row, and a consolidated specification range string.
What the extracted data looks like
Below is a sample of the structured output. Each row in the JSON corresponds to one specification attribute from the comparison table, with values captured per trim configuration.
[
{
"car_features": "Motor",
"feature_value": "2.3",
"feature_value_2": "2.3",
"feature_value_3": "2.3",
"table_row_class": "seo-ui-product-comparison-table__tbody__tr",
"vehicle_specification": "2.3"
},
{
"car_features": "Pot\u00eancia",
"feature_value": "136 hp",
"feature_value_2": "136 hp",
"feature_value_3": "130 hp",
"table_row_class": "seo-ui-product-comparison-table__tbody__tr",
"vehicle_specification": "136 hp"
},
{
"car_features": "Comprimento",
"feature_value": "6225 mm",
"feature_value_2": "5575 mm",
"feature_value_3": "6225 mm | 5575 mm",
"table_row_class": "seo-ui-product-comparison-table__tbody__tr hidden",
"vehicle_specification": "5075 mm | 6225 mm"
}
]The table_row_class field encodes the CSS visibility state of each row. Rows with the hidden class are collapsed by default in the browser but are still captured in full by Minexa.ai. The vehicle_specification field returns a pipe-separated range string when a specification varies across body lengths, such as overall length or roof height. The feature_value_2 and feature_value_3 fields surface per-trim values so you can compare configurations side by side in a single dataset row.
Install the Minexa.ai Chrome extension to start extracting vehicle specification data from Mercado Livre today.
Step 8: Run the job and export your data
The scraping job appears in your job list with a Run button. Click it to start extraction. Results populate in a table as pages are processed.
Once the job finishes, you can export the full dataset to Excel, JSON, or push it directly to Google Sheets. Each specification attribute lands in its own column, each row represents one feature from the comparison table, and the output is ready to use without any cleanup.
Once the scraper is trained, you can reuse it on any structurally similar Mercado Livre catalogue page without repeating the setup. The same configuration works across different vehicle models or model years as long as the page layout matches.
If you are building a broader supply chain or fleet intelligence dataset, you may also find this post useful: How to scrape real estate data from QuintoAndar using the Minexa API, which covers extracting structured listing data from another major Brazilian marketplace using the same underlying workflow.
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