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name: enriching-tables-with-web-data description: Enriches Excel tables with up-to-date web data using Bright Data MCP tools. Use when the user asks to enrich, update, or add information to spreadsheet data from LinkedIn, Instagram, Amazon, e-commerce sites, social media, or any web source. Supports company profiles, product data, social media metrics, reviews, and more.

Table Enrichment with Bright Data

Enrich Excel tables with current web data using Bright Data's 60+ MCP tools.

Requirements

Install openpyxl for Excel file handling:

pip install openpyxl --break-system-packages

Workflow

1. Analyze the table

Read the Excel file and identify:

• Columns that contain identifiers (URLs, usernames, product IDs, company names)
• Columns that need enrichment (empty or to be updated)
• Data type (social media, e-commerce, business, etc.)

import openpyxl

workbook = openpyxl.load_workbook("file.xlsx")
sheet = workbook.active

# Read headers and first few rows to understand structure
headers = [cell.value for cell in sheet[1]]
sample_data = [[cell.value for cell in row] for row in list(sheet.rows)[1:6]]

2. Match tools to data type

Based on identifiers in the table, select appropriate Bright Data tools:

Social Media:

• BrightData:web_data_linkedin_person_profile - LinkedIn profiles
• BrightData:web_data_linkedin_company_profile - LinkedIn companies
• BrightData:web_data_instagram_profiles - Instagram profiles
• BrightData:web_data_tiktok_profiles - TikTok profiles
• BrightData:web_data_youtube_profiles - YouTube channels

E-commerce:

• BrightData:web_data_amazon_product - Amazon products
• BrightData:web_data_walmart_product - Walmart products
• BrightData:web_data_ebay_product - eBay products
• BrightData:web_data_etsy_products - Etsy products

Business:

• BrightData:web_data_crunchbase_company - Company data
• BrightData:web_data_zoominfo_company_profile - ZoomInfo data

General:

• BrightData:scrape_as_markdown - Any public webpage

3. Enrich data row by row

For each row, call the appropriate MCP tool and extract relevant fields:

# Example: Enriching LinkedIn profiles
for row_idx, row in enumerate(sheet.iter_rows(min_row=2), start=2):
    linkedin_url = row[url_column_idx].value
    
    if linkedin_url:
        # Call BrightData:web_data_linkedin_person_profile
        # Extract: name, title, company, location, connections, etc.
        # Write to corresponding columns
        
        sheet.cell(row=row_idx, column=name_col).value = profile_data['name']
        sheet.cell(row=row_idx, column=title_col).value = profile_data['title']

4. Handle errors and missing data

• Skip rows with invalid/missing identifiers
• Log which rows were enriched successfully
• Note rate limits or tool failures
• Preserve original data

5. Save enriched file

Save to /mnt/user-data/outputs/:

output_path = '/mnt/user-data/outputs/enriched_table.xlsx'
workbook.save(output_path)

Provide link: [View enriched table](computer:///mnt/user-data/outputs/enriched_table.xlsx)

Tool selection logic

If table has LinkedIn URLs → Use BrightData:web_data_linkedin_person_profile or BrightData:web_data_linkedin_company_profile

If table has Instagram URLs → Use BrightData:web_data_instagram_profiles

If table has Amazon URLs (with /dp/) → Use BrightData:web_data_amazon_product

If table has product names but no URLs → Use BrightData:search_engine to find URLs first, then scrape

If table has company names → Search for LinkedIn/Crunchbase URLs, then enrich

If URLs are present but platform unknown → Use BrightData:scrape_as_markdown

Best practices

• Batch similar requests: Group rows by data type before calling tools
• Start small: Test enrichment on first 3-5 rows before processing entire table
• Preserve originals: Create new columns for enriched data
• Show progress: Update user after every 10-20 rows processed
• Handle nulls: Explicitly mark cells where data couldn't be retrieved

Example enrichment types

LinkedIn profiles → Add: job title, company, location, connections, industry

Instagram accounts → Add: followers, posts count, engagement rate, bio

Amazon products → Add: price, rating, review count, availability, seller

Company names → Add: LinkedIn URL, website, employee count, funding, industry

Generic URLs → Add: page title, description, key content, last updated