Web research assistant: automated search & scraping with Gemini AI and spreadsheet reports

⚠️ IMPORTANT: This template requires self-hosted n8n hosting due to the use of community nodes (MCP tools). It will not work on n8n Cloud. Make sure you have access to a self-hosted n8n instance before using this template.

Overview

This workflow automation allows a Google Gemini-powered AI Agent to orchestrate multi-source web intelligence using MCP (Model Context Protocol) tools such as Firecrawl, Brave Search, and Apify.

The system allows users to interact with the agent in natural language, which then leverages various external data collection tools, processes the results, and automatically organizes them into structured spreadsheets.

With built-in memory, flexible tool execution, and conversational capabilities, this workflow acts as a multi-agent research assistant, capable of retrieving, synthesizing, and delivering actionable insights in real time.

How the system works

AI Agent + MCP Pipeline

1. User Interaction A chat message is received and forwarded to the AI Agent.

2. AI Orchestration The agent, powered by Google Gemini, decides which MCP tools to invoke based on the query.

   • Firecrawl-MCP: Recursive web crawling and content extraction.
   • Brave-MCP: Real-time web search with structured results.
   • Apify-MCP: Automation of web scraping tasks with scalable execution.

3. Memory Management A memory module stores context across conversations, ensuring multi-turn reasoning and task continuity.

4. Spreadsheet automation Results are structured in a new, automatically created Google Spreadsheet, enriched with formatting and additional metadata.

5. Data processing The workflow generates the spreadsheet content, updates the sheet, and improves results via HTTP requests and field edits.

6. Delivery of results Users receive a structured and contextualized dataset ready for review, analysis, or integration into other systems.

Configuration instructions

Estimated setup time: 45 minutes

Prerequisites

• Self-hosted n8n instance (v0.200.0 or higher recommended)
• Google Gemini API key
• MCP-compatible nodes (Firecrawl, Brave, Apify) configured
• Google Sheets credentials for spreadsheet automation

Detailed configuration steps

Step 1: Configuring the AI Agent

• AI Agent node:
  • Select Google Gemini as the LLM model
  • Configure your Google Gemini API key in the n8n credentials
  • Set the system prompt to guide the agent's behavior
  • Connect the Simple Memory node to enable context tracking

Step 2: Integrating MCP Tools

• Firecrawl-MCP Configuration:

  • Install the @n8n/n8n-nodes-firecrawl-mcp package
  • Configure your Firecrawl API key
  • Set crawling parameters (depth, CSS selectors)

• Brave-MCP configuration:

  • Install the @n8n/n8n-nodes-brave-mcp package
  • Add your Brave Search API key
  • Configure search filters (region, language, SafeSearch)

• Apify-MCP configuration:

  • Install the @n8n/n8n-nodes-apify-mcp package
  • Configure your Apify credentials
  • Select the appropriate actors for your use cases

Step 3: Spreadsheet automation

• “Create Spreadsheet” node:

  • Configure Google Sheets authentication (OAuth2 or Service Account)
  • Set the file name with dynamic timestamps
  • Specify the destination folder in Google Drive

• “Generate Spreadsheet Content” node:

  • Transform the agent's outputs into tabular format
  • Define the columns: URL, Title, Description, Source, Timestamp
  • Configure data formatting (dates, links, metadata)

• “Update Spreadsheet” node:

  • Insert the data into the created sheet
  • Apply automatic formatting (headers, colors, column widths)
  • Add summary formulas if necessary

Step 4: Post-processing and delivery

• “Data Enrichment Request” node (formerly “HTTP Request1”):

  • Configure optional API calls to enrich the data
  • Add additional metadata (geolocation, sentiment, categorization)
  • Manage errors and timeouts

• “Edit Fields” node:

  • Refine the final dataset (metadata, tags, filters)
  • Clean and normalize the data
  • Prepare the final response for the user

Structure of generated Google Sheets

Default columns

Column	Description	Type
URL	Data source URL	Hyperlink
Title	Page/resource title	Text
Description	Description or content excerpt	Long text
Source	MCP tool used (Brave/Firecrawl/Apify)	Text
Timestamp	Date/time of collection	Date/Time
Metadata	Additional data (JSON)	Text

Automatic formatting

• Headings: Bold font, colored background
• URLs: Formatted as clickable links
• Dates: Standardized ISO 8601 format
• Columns: Width automatically adjusted to content

Use cases

Business and enterprise

• Competitive analysis combining search, crawling, and structured scraping
• Market trend research with multi-source aggregation
• Automated reporting pipelines for business intelligence

Research and academia

• Literature discovery across multiple sources
• Data collection for research projects
• Automated bibliographic extraction from online sources

Engineering and development

• Discovery of APIs and documentation
• Aggregation of product information from multiple platforms
• Scalable structured scraping for datasets

Personal productivity

• Automated creation of newsletters or knowledge hubs
• Personal research assistant compiling spreadsheets from various online data

Key features

Multi-source intelligence

• Firecrawl for deep crawling
• Brave for real-time search
• Apify for structured web scraping

AI-driven orchestration

• Google Gemini for reasoning and tool selection
• Memory for multi-turn interactions
• Context-based adaptive workflows

Structured data output

• Automatic spreadsheet creation
• Data enrichment and formatting
• Ready-to-use datasets for reporting

Performance and scalability

• Handles multiple simultaneous tool calls
• Scalable web data extraction
• Real-time aggregation from multiple MCPs

Security and privacy

• Secure authentication based on API keys
• Data managed in Google Sheets / n8n
• Configurable retention and deletion policies

Technical architecture

Workflow

User query → AI agent (Gemini) → MCP tools (Firecrawl / Brave / Apify) → Aggregated results → Spreadsheet creation → Data processing → Results delivery

Supported data types

• Text and metadata from crawled web pages
• Search results from Brave queries
• Structured data from Apify scrapers
• Tabular reports via Google Sheets

Integration options

Chat interfaces

• Web widget for conversational queries
• Slack/Teams chatbot integration
• REST API access points

Data sources

• Websites (via Firecrawl/Apify)
• Search engines (via Brave)
• APIs (via HTTP Request enrichment)

Performance specifications

• Query response: < 5 seconds (search tasks)
• Crawl capacity: Thousands of pages per run
• Spreadsheet automation: Real-time creation and updates
• Accuracy: > 90% when using combined sources

Advanced configuration options

Customization

• Set custom prompts for the AI Agent
• Adjust the spreadsheet schema for reporting needs
• Configure retries for failed tool runs

Analytics and monitoring

• Track tool usage and costs
• Monitor crawl and search success rates
• Log queries and outputs for auditing

Troubleshooting and support

• Timeouts: Manually re-run failed MCP executions
• Data gaps: Validate Firecrawl/Apify selectors
• Spreadsheet errors: Check Google Sheets API quotas