search-web

Brave Deep Research

suthio

by suthio

Brave Deep Research combines Brave Search with advanced web scraper tools to extract and traverse web content for thorou

Combines Brave Search with web scraping to provide deep research capabilities by extracting full content from pages and traversing links at configurable depths

github stars

5

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Configurable link traversal depthIntelligent content extractionPuppeteer-powered scraping

best for

  • / Researchers needing comprehensive information beyond search snippets
  • / Content creators gathering detailed background material
  • / Analysts requiring multi-page context on topics

capabilities

  • / Search web using Brave Search API
  • / Extract full content from web pages
  • / Follow links to explore related pages
  • / Filter out navigation and ads from content
  • / Configure scraping depth and timeouts
  • / Process multiple pages in batch

what it does

Combines Brave Search with web scraping to extract full page content and follow links for comprehensive research. Goes beyond search snippets to provide complete webpage text at configurable depths.

about

Brave Deep Research is a community-built MCP server published by suthio that provides AI assistants with tools and capabilities via the Model Context Protocol. Brave Deep Research combines Brave Search with advanced web scraper tools to extract and traverse web content for thorou It is categorized under search web.

how to install

You can install Brave Deep Research in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Brave Deep Research is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

@suthio/brave-deep-research-mcp

A Model Context Protocol (MCP) server that combines Brave Search with Puppeteer-powered content extraction for deep research capabilities. This server allows AI assistants to perform comprehensive web searches by not only retrieving search results but also visiting the pages to extract full content and explore linked pages.

Comparison with Standard Brave Search MCP Server

Standard Brave Search MCP Server:

  • Search Capability: Uses the Brave Search API to perform basic web searches
  • Data Retrieval: Returns only the search results (title, URL, and snippet) provided by the API
  • Content Depth: No access to full webpage content beyond the search snippets
  • Page Exploration: No ability to visit pages or follow links
  • Information Scope: Limited to the brief information available in search results
  • Content Processing: No content extraction or cleaning capabilities
  • Customization: Limited to basic search parameters (query, count, offset)
  • Use Case: Best for quick searches where only an overview is needed

Brave Deep Research MCP Server (this project):

  • Search Capability: Uses Brave Search API for initial results, then enhances with web scraping
  • Data Retrieval: Extracts complete page content from each search result
  • Content Depth: Provides full webpage content with main text extraction
  • Page Exploration: Can traverse links to explore related content at configurable depths
  • Information Scope: Accesses comprehensive information across multiple related pages
  • Content Processing: Intelligently identifies and extracts main content, filtering out navigation, ads, footers, etc.
  • Customization: Configurable depth of exploration, result count, headless mode, and timeouts
  • Use Case: Ideal for in-depth research requiring detailed information and context

Practical Differences in an Example Query

For a query like "climate change mitigation technologies":

Standard Brave Search MCP:

Title: "Latest Climate Change Mitigation Technologies - Example Site"
URL: "https://example.com/climate-tech"
Snippet: "Various technologies are being developed to mitigate climate change, including carbon capture..."

(Limited to just these search result snippets)

Brave Deep Research MCP:

# Latest Climate Change Mitigation Technologies - Example Site
URL: https://example.com/climate-tech

## Content
Carbon capture and storage (CCS) technology has advanced significantly in recent years. The latest direct air capture facilities can now remove CO2 at a cost of $250 per ton, down from $600 just five years ago. Implementation challenges remain, including...

[Followed by several pages of detailed content from the original page and linked pages]

Features

  • Deep Search: Go beyond search results to extract complete page content
  • Configurable Depth: Specify how many levels of links to follow from initial results
  • Content Extraction: Intelligently identify and extract main content from pages
  • Metadata Extraction: Get titles, descriptions, and structured content
  • Debug Mode: Configurable logging for troubleshooting
  • Headless Mode Toggle: Run browser in visible or headless mode

Installation

# Install from npm
npm install -g @suthio/brave-deep-research-mcp

# Or clone the repository
git clone https://github.com/suthio/brave-deep-research-mcp.git
cd brave-deep-research-mcp
npm install
npm run build

Configuration

Create a .env file based on the provided .env.example:

# Copy the example env file
cp .env.example .env

# Edit the file to add your Brave API key and other settings
nano .env

Environment Variables

  • BRAVE_API_KEY: Your Brave Search API key (required)
  • PUPPETEER_HEADLESS: Whether to run Puppeteer in headless mode (default: true)
  • PAGE_TIMEOUT: Timeout for page loading in milliseconds (default: 30000)
  • DEBUG_MODE: Enable detailed debug logging (default: false)

Usage

Running from command line

# If installed globally via npm
brave-deep-research-mcp

# Or run directly from the package
npx @suthio/brave-deep-research-mcp

# Or run locally after cloning
npm start

Using with Claude for Desktop

To use this server with Claude for Desktop:

  1. Install the package:
npm install -g @suthio/brave-deep-research-mcp
  1. Edit the Claude for Desktop configuration file:

    • On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • On Windows: %APPDATA%\Claude\claude_desktop_config.json
  2. Add the following to the mcpServers section:

{
  "mcpServers": {
    "brave-deep-research": {
      "command": "npx",
      "args": ["@suthio/brave-deep-research-mcp"],
      "env": {
        "BRAVE_API_KEY": "your_brave_api_key_here",
        "PUPPETEER_HEADLESS": "true"
      }
    }
  }
}
  1. Restart Claude for Desktop
  2. You can now use the deep-search tool in your conversations

Example Queries

  • "Use deep-search to research the latest developments in quantum computing"
  • "Perform a deep search on climate change mitigation strategies with depth 2"
  • "Deep search for information about sustainable architecture, with 5 results"

Tool Parameters

The deep-search tool accepts the following parameters:

  • query (required): The search query
  • results (optional): Number of search results to process (default: 3, max: 10)
  • depth (optional): Depth of link traversal for each result (default: 1, max: 3)

Development

# Clone the repository
git clone https://github.com/suthio/brave-deep-research-mcp.git
cd brave-deep-research-mcp

# Install dependencies
npm install

# Run in development mode
npm run dev

# Build the project
npm run build

How It Works

  1. The tool first performs a search using the Brave Search API to get initial results
  2. For each search result, it launches a Puppeteer browser to visit the page
  3. It extracts the main content, metadata, and links from each page
  4. If depth > 1, it follows links on the page and repeats the process
  5. All extracted content is formatted and returned to the AI assistant

License

MIT

FAQ

What is the Brave Deep Research MCP server?
Brave Deep Research is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Brave Deep Research?
This profile displays 25 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

Use Cases

Web Research & Information Gathering

Fetch and extract information from websites automatically

Example

Research competitor pricing, scrape product reviews, monitor news mentions

Automate 5-10 hours/week of manual web research

Content Monitoring & Alerts

Track website changes, new content, price updates

Example

Monitor competitor blog for new posts, track stock availability, watch for pricing changes

Stay informed without manual checking, never miss important updates

Data Extraction & Aggregation

Extract structured data from multiple websites

Example

Compile product listings from 10 e-commerce sites, aggregate job postings, collect real estate data

Build datasets 100x faster than manual copying

API-less Integration

Interact with services that don't offer APIs

Example

Check form submissions, validate website functionality, test user flows

Automate interactions with any website, even without API

Implementation Guide

Prerequisites

  • Claude Desktop or Cursor with MCP support
  • Understanding of web scraping ethics and robots.txt
  • Rate limiting awareness to avoid overwhelming target sites
  • Knowledge of legal restrictions on data collection

Time Estimate

20-40 minutes including configuration and testing

Installation Steps

  1. 1.Install web automation MCP server via npm or pip
  2. 2.Configure allowed domains and rate limits in MCP config
  3. 3.Test with simple fetch: 'Get content from example.com'
  4. 4.Progress to extraction: 'Extract all product prices from this page'
  5. 5.Set up monitoring: 'Check this URL daily for changes'
  6. 6.Parse structured data: 'Create CSV from this table'
  7. 7.Respect robots.txt and rate limits always

Troubleshooting

  • 403 Forbidden: Website blocks bots—respect their wishes, use official API instead
  • Rate limit errors: Slow down requests, add delays between fetches
  • Stale data: Target site changed HTML structure—update selectors
  • Timeout errors: Site is slow or blocking—increase timeout, try different user agent
  • JavaScript-rendered content: Use headless browser MCP servers for dynamic sites

Best Practices

✓ Do

  • +Check robots.txt and respect crawl rules
  • +Rate limit requests: 1-2 requests/second maximum
  • +Use official APIs when available instead of scraping
  • +Identify your bot with descriptive user agent
  • +Cache results to minimize repeated requests
  • +Handle errors gracefully with retries and fallbacks
  • +Validate extracted data for accuracy

✗ Don't

  • Don't scrape sites that explicitly forbid it (robots.txt, ToS)
  • Don't overwhelm servers with rapid requests—use rate limiting
  • Don't scrape personal data without consent and legal basis
  • Don't ignore copyright on extracted content
  • Don't assume HTML structure is stable—handle changes
  • Don't use scraped data for commercial purposes without permission

💡 Pro Tips

  • Use CSS selectors or XPath for robust data extraction
  • Set up monitoring alerts for extraction failures (structure changed)
  • Implement exponential backoff for retries on failures
  • Store raw HTML for reprocessing if extraction logic changes
  • Combine with data analysis tools for insights from extracted data
  • Consider using official APIs or RSS feeds as more stable alternatives

Technical Details

Architecture

MCP server handles HTTP requests, HTML parsing, JavaScript rendering (if headless browser), and returns structured data to Claude.

Protocols

  • HTTP/HTTPS
  • WebSocket (for real-time sites)
  • Puppeteer/Playwright (for JavaScript sites)

Compatibility

  • Static HTML sites
  • JavaScript-rendered SPAs (with headless browser)
  • REST APIs
  • GraphQL endpoints

When to Use This

✓ Use When

Use for research automation, content monitoring, data aggregation from multiple sources, and when official APIs don't exist. Best for read-only information gathering.

✗ Avoid When

Avoid for sites with APIs (use API instead), sites that explicitly forbid scraping, when data is copyrighted, or for login-required content without proper authorization.

Integration

  • Scheduled monitoring with change detection
  • Multi-source data aggregation pipelines
  • Fallback to web scraping when API rate limits hit
  • Headless browser for JavaScript-heavy sites

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.625 reviews
  • Neel Gonzalez· Dec 28, 2024

    We wired Brave Deep Research into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Dhruvi Jain· Dec 24, 2024

    Strong directory entry: Brave Deep Research surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Pratham Ware· Dec 4, 2024

    Brave Deep Research is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Oshnikdeep· Nov 15, 2024

    Brave Deep Research has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Ganesh Mohane· Oct 6, 2024

    According to our notes, Brave Deep Research benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Sakshi Patil· Sep 25, 2024

    Brave Deep Research is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Kwame Bhatia· Sep 5, 2024

    Strong directory entry: Brave Deep Research surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Nikhil Perez· Aug 24, 2024

    I recommend Brave Deep Research for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Chaitanya Patil· Aug 16, 2024

    We evaluated Brave Deep Research against two servers with overlapping tools; this profile had the clearer scope statement.

  • Zara Thompson· Aug 8, 2024

    Strong directory entry: Brave Deep Research surfaces stars and publisher context so we could sanity-check maintenance before adopting.

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