
Brave Deep Research
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.
Combines Brave Search with web scraping to provide deep research capabilities by extracting full content from pages and traversing links at configurable depths
What it does
- 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
Best for
About Brave Deep Research
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
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.
@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:
- Install the package:
npm install -g @suthio/brave-deep-research-mcp
-
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
- On macOS:
-
Add the following to the
mcpServerssection:
{
"mcpServers": {
"brave-deep-research": {
"command": "npx",
"args": ["@suthio/brave-deep-research-mcp"],
"env": {
"BRAVE_API_KEY": "your_brave_api_key_here",
"PUPPETEER_HEADLESS": "true"
}
}
}
}
- Restart Claude for Desktop
- 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 queryresults(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
- The tool first performs a search using the Brave Search API to get initial results
- For each search result, it launches a Puppeteer browser to visit the page
- It extracts the main content, metadata, and links from each page
- If depth > 1, it follows links on the page and repeats the process
- All extracted content is formatted and returned to the AI assistant
License
MIT
Alternatives
Related Skills
Browse all skillsComprehensive research, analysis, and content extraction system. USE WHEN user says 'research' (ANY form - this is the MANDATORY trigger), 'do research', 'extensive research', 'quick research', 'minor research', 'research this', 'find information', 'investigate', 'extract wisdom', 'extract alpha', 'analyze content', 'can't get this content', 'use fabric', OR requests any web/content research. Supports three research modes (quick/standard/extensive), deep content analysis, intelligent retrieval, and 242+ Fabric patterns. NOTE: For due diligence, OSINT, or background checks, use OSINT skill instead.
GPT Researcher is an autonomous deep research agent that conducts web and local research, producing detailed reports with citations. Use this skill when helping developers understand, extend, debug, or integrate with GPT Researcher - including adding features, understanding the architecture, working with the API, customizing research workflows, adding new retrievers, integrating MCP data sources, or troubleshooting research pipelines.
Tavily AI search platform with 5 modes: Search (web/news/finance), Extract (URL content), Crawl (website crawling), Map (sitemap discovery), and Research (deep research with citations). Use for: web search with LLM answers, content extraction, site crawling, deep research.
Expert web researcher using advanced search techniques and synthesis. Masters search operators, result filtering, and multi-source verification. Handles competitive analysis and fact-checking. Use PROACTIVELY for deep research, information gathering, or trend analysis.
Neural web search via Exa AI. Search people, companies, news, research, code. Supports deep search, domain filters, date ranges.
This skill should be used when the user asks to "유튜브 정리", "영상 요약", "transcript 번역", "YouTube digest", "영상 퀴즈", or provides a YouTube URL for analysis. Extracts transcript, generates summary/insights/Korean translation, and tests comprehension with 9 quiz questions across 3 difficulty levels. Optional Deep Research for web-based follow-up.