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Extract, summarize & analyze Amazon price drops with Bright Data & Google Gemini

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Extract, summarize & analyze Amazon price drops with Bright Data & Google Gemini preview
Open on n8n.io

Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Notice Community nodes can only be installed on self hosted instances of n8n. Who this ...

Best for

  • Market Research automation workflows
  • AI Summarization automation workflows
  • advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.manualtrigger, n8n-nodes-mcp.mcpclient, n8n-nodes-base.set, n8n-nodes-base.stickynote, n8n-nodes-base.splitout, n8n-nodes-base.splitinbatches, n8n-nodes-base.wait, @n8n/n8n-nodes-langchain.chainsummarization

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Ranjan Dailata.

Original n8n.io source

1.1 Workflow description

Title
Extract, summarize & analyze Amazon price drops with Bright Data & Google Gemini
Workflow name
Extract, summarize & analyze Amazon price drops with Bright Data & Google Gemini

Notice

Community nodes can only be installed on self-hosted instances of n8n.

Who this is for

This n8n-powered automation uses Bright Data's MCP Client to extract real-time data from a price drop site listing the amazon products, including price changes and related product details.

The extracted data is enriched with structured data transformation, content summarization, and sentiment analysis using Google Gemini LLM.

The Amazon Price Drop Intelligence Engine is designed for:

  • Ecommerce Analysts who need timely updates on competitor pricing trends

  • Brand Managers seeking to understand consumer sentiment around pricing

  • Data Scientists building pricing models or enrichment pipelines

  • Affiliate Marketers looking to optimize campaigns based on dynamic pricing

  • AI Developers automating product intelligence pipelines

What problem is this workflow solving?

This workflow solves several key pain points:

Reliable Scraping: Uses Bright Data MCP, a managed crawling platform that handles proxies, captchas, and site structure changes automatically.

Insight Generation: Transforms unstructured HTML into structured data and then into human-readable summaries using Google Gemini LLM.

Sentiment Context: Goes beyond raw pricing data to reveal how customers feel about the price change, helping businesses and researchers measure consumer reaction.

Automated Reporting: Aggregates and stores data for easy access and downstream automation (e.g., dashboards, notifications, pricing models).

What this workflow does

Scrape price drop site with Bright Data MCP

The workflow begins by scraping targeted price drop site for Amazon listings using Bright Data's Model Context Protocol (MCP).

You can configure this to target:

Structured Data Extraction

Once the HTML content is retrieved, Google Gemini is employed to:

  • Parse and structure the product information (title, price, discount, brand, ratings)

Summarization & Sentiment Analysis

The extracted data is passed through an LLM chain to:

  • Generate a concise summary of the product and its recent price movement

  • Perform sentiment analysis on user reviews and public perception

Store the Results

  • Save to disk for archiving or bulk processing

  • Updated in a Google Sheet, making it instantly shareable with your team or integrated into a BI dashboard

Pre-conditions

  1. Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol
  2. You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below.
  3. You need to have the Google Gemini API Key. Visit Google AI Studio
  4. You need to install the Bright Data MCP Server @brightdata/mcp
  5. You need to install the n8n-nodes-mcp

Setup

  1. Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp
  2. Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine.
  3. Sign up at Bright Data.
  4. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel.
  5. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions.
  6. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy).
  7. In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below.

Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token>

How to customize this workflow to your needs

  • Target different platforms: Switch Amazon for Walmart, eBay, or any ecommerce source using Bright Data’s flexible scraping infrastructure.

  • Enrich with more LLM tasks: Add brand tone analysis, category classification, or competitive benchmarking using Gemini prompts.

  • Visualize output: Pipe the Google Sheet to Looker Studio, Tableau, or Power BI.

  • Notification integrations: Add Slack, Discord, or email notifications for price drop alerts.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - When clicking ‘Test workflow’

Type / Role
n8n-nodes-base.manualTrigger - manualTrigger
Config choices
Version 1

Block 2 - Bright Data MCP Client List Tools

Type / Role
n8n-nodes-mcp.mcpClient - mcpClient
Config choices
Version 1

Block 3 - Set input fields

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 4 - Sticky Note2

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 5 - Sticky Note4

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 6 - Sticky Note5

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 7 - Sticky Note3

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 8 - Split Out

Type / Role
n8n-nodes-base.splitOut - splitOut
Config choices
Version 1

Block 9 - Loop Over Items

Type / Role
n8n-nodes-base.splitInBatches - splitInBatches
Config choices
Version 3

Block 10 - Wait

Type / Role
n8n-nodes-base.wait - wait
Config choices
Version 1.1

Block 11 - Summarize Content

Type / Role
@n8n/n8n-nodes-langchain.chainSummarization - chainSummarization
Config choices
Version 2.1

Block 12 - Sentiment Analysis

Type / Role
@n8n/n8n-nodes-langchain.informationExtractor - informationExtractor
Config choices
Version 1.1

Block 13 - Google Gemini Chat Model for Summarize Content

Type / Role
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices
Version 1

Block 14 - Google Gemini Chat Model for Sentiment Analysis

Type / Role
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices
Version 1

Block 15 - Merge

Type / Role
n8n-nodes-base.merge - merge
Config choices
Version 3.1

Block 16 - Update Google Sheets

Type / Role
n8n-nodes-base.googleSheets - googleSheets
Config choices
Version 4.5

Block 17 - Webhook Notification for Price Drop Info

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.2

Block 18 - Google Gemini Chat Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices
Version 1

Block 19 - Structured Output Parser

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.2

Block 20 - Structure Data Extract Using LLM

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.7

Block 21 - MCP Client for Price Drop Data Extract

Type / Role
n8n-nodes-mcp.mcpClient - mcpClient
Config choices
Version 1

Block 22 - MCP Client for Price Drop Data Extract Within a Loop

Type / Role
n8n-nodes-mcp.mcpClient - mcpClient
Config choices
Version 1

Block 23 - Aggregate

Type / Role
n8n-nodes-base.aggregate - aggregate
Config choices
Version 1

Block 24 - Recursive Character Text Splitter

Type / Role
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - textSplitterRecursiveCharacterTextSplitter
Config choices
Version 1

Showing the first 24 of 26 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow Extract, summarize & analyze Amazon price drops with Bright Data & Google Gemini
Complexity advanced
Nodes 26
Categories Market Research, AI Summarization
Author Ranjan Dailata
Published 02 Jun 2025

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/4611/4611.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does Extract, summarize & analyze Amazon price drops with Bright Data & Google Gemini do?

Notice Community nodes can only be installed on self hosted instances of n8n. Who this ...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Market Research, AI Summarization use case.