Search · MODEL CONTEXT PROTOCOL
PedroDnT mcp DEEPwebresearch
Enhanced MCP server for deep web research
WHAT IT CONNECTS
What PedroDnT mcp DEEPwebresearch gives an AI Agent
- Intelligent Search Queue System
- Batch search operations with rate limiting
- Queue management with progress tracking
- Error recovery and automatic retries
- Search result deduplication
- Enhanced Content Extraction
- TF-IDF based relevance scoring
- Keyword proximity analysis
- Content section weighting
- Readability scoring
- Improved HTML structure parsing
- Structured data extraction
- Better content cleaning and formatting
- Core Features
- Google search integration
- Webpage content extraction
- Research session tracking
- Markdown conversion with improved formatting
SECURITY
An MCP listing is not a security review
An MCP server may receive model context, credentials, local files, or permission to call external systems. Review its code, requested environment variables, network behavior, package provenance, and maintenance status before connecting it to an agent.
README
Repository documentation
A Model Context Protocol (MCP) server for advanced web research.
- Added visit_page tool for direct webpage content extraction
- Optimized performance to work within MCP timeout limits
- Reduced default maxDepth and maxBranching parameters
- Improved page loading efficiency
- Added timeout checks throughout the process
- Enhanced error handling for timeouts
This project is a fork of mcp-webresearch by mzxrai, enhanced with additional features for deep web research capabilities. We're grateful to the original creators for their foundational work.
Bring real-time info into Claude with intelligent search queuing, enhanced content extraction, and deep research capabilities.
-
Intelligent Search Queue System
- Batch search operations with rate limiting
- Queue management with progress tracking
- Error recovery and automatic retries
- Search result deduplication
-
Enhanced Content Extraction
- TF-IDF based relevance scoring
- Keyword proximity analysis
- Content section weighting
- Readability scoring
- Improved HTML structure parsing
- Structured data extraction
- Better content cleaning and formatting
-
Core Features
- Google search integration
- Webpage content extraction
- Research session tracking
- Markdown conversion with improved formatting
- Node.js >= 18 (includes
npmandnpx) - Claude Desktop app
To install Deep Web Research Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @PedroDnT/mcp-deepwebresearch --client claude# Install globally using npm
npm install -g mcp-deepwebresearch
# Or using yarn
yarn global add mcp-deepwebresearch
# Or using pnpm
pnpm add -g mcp-deepwebresearch# Using npm
npm install mcp-deepwebresearch
# Using yarn
yarn add mcp-deepwebresearch
# Using pnpm
pnpm add mcp-deepwebresearchAfter installing the package, add this entry to your claude_desktop_config.json:
{
"mcpServers": {
"deepwebresearch": {
"command": "mcp-deepwebresearch",
"args": []
}
}
}Location: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"deepwebresearch": {
"command": "mcp-deepwebresearch",
"args": []
}
}
}Location: ~/Library/Application Support/Claude/claude_desktop_config.json
This config allows Claude Desktop to automatically start the web research MCP server when needed.
After installation, run this command to install required browser dependencies:
npx playwright install chromiumSimply start a chat with Claude and send a prompt that would benefit from web research. If you'd like a prebuilt prompt customized for deeper web research, you can use the agentic-research prompt that we provide through this package. Access that prompt in Claude Desktop by clicking the Paperclip icon in the chat input and then selecting Choose an integration → deepwebresearch → agentic-research.
-
deep_research- Performs comprehensive research with content analysis
- Arguments:
{ topic: string; maxDepth?: number; // default: 2 maxBranching?: number; // default: 3 timeout?: number; // default: 55000 (55 seconds) minRelevanceScore?: number; // default: 0.7 }
- Returns:
{ findings: { mainTopics: Array<{name: string, importance: number}>; keyInsights: Array<{text: string, confidence: number}>; sources: Array<{url: string, credibilityScore: number}>; }; progress: { completedSteps: number; totalSteps: number; processedUrls: number; }; timing: { started: string; completed?: string; duration?: number; operations?: { parallelSearch?: number; deduplication?: number; topResultsProcessing?: number; remainingResultsProcessing?: number; total?: number; }; }; }
-
parallel_search- Performs multiple Google searches in parallel with intelligent queuing
- Arguments:
{ queries: string[], maxParallel?: number } - Note: maxParallel is limited to 5 to ensure reliable performance
-
visit_page- Visit a webpage and extract its content
- Arguments:
{ url: string } - Returns:
{ url: string; title: string; content: string; // Markdown formatted content }
A guided research prompt that helps Claude conduct thorough web research. The prompt instructs Claude to:
- Start with broad searches to understand the topic landscape
- Prioritize high-quality, authoritative sources
- Iteratively refine the research direction based on findings
- Keep you informed and let you guide the research interactively
- Always cite sources with URLs
The server can be configured through environment variables:
MAX_PARALLEL_SEARCHES: Maximum number of concurrent searches (default: 5)SEARCH_DELAY_MS: Delay between searches in milliseconds (default: 200)MAX_RETRIES: Number of retry attempts for failed requests (default: 3)TIMEOUT_MS: Request timeout in milliseconds (default: 55000)LOG_LEVEL: Logging level (default: 'info')
-
Rate Limiting
- Symptom: "Too many requests" error
- Solution: Increase
SEARCH_DELAY_MSor decreaseMAX_PARALLEL_SEARCHES
-
Network Timeouts
- Symptom: "Request timed out" error
- Solution: Ensure requests complete within the 60-second MCP timeout
-
Browser Issues
- Symptom: "Browser failed to launch" error
- Solution: Ensure Playwright is properly installed (
npx playwright install)
This is beta software. If you run into issues:
-
Check Claude Desktop's MCP logs:
# On macOS tail -n 20 -f ~/Library/Logs/Claude/mcp*.log # On Windows Get-Content -Path "$env:APPDATA\Claude\logs\mcp*.log" -Tail 20 -Wait
-
Enable debug logging:
export LOG_LEVEL=debug
# Install dependencies
pnpm install
# Build the project
pnpm build
# Watch for changes
pnpm watch
# Run in development mode
pnpm dev# Run all tests
pnpm test
# Run tests in watch mode
pnpm test:watch
# Run tests with coverage
pnpm test:coverage# Run linter
pnpm lint
# Fix linting issues
pnpm lint:fix
# Type check
pnpm type-check- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
- Follow TypeScript best practices
- Maintain test coverage above 80%
- Document new features and APIs
- Update CHANGELOG.md for significant changes
- Follow semantic versioning
- Use batch operations where possible
- Implement proper error handling and retries
- Consider memory usage with large datasets
- Cache results when appropriate
- Use streaming for large content
- Node.js >= 18
- Playwright (automatically installed as a dependency)
- macOS
- Windows
- Linux
MIT
This project builds upon the excellent work of mcp-webresearch by mzxrai. The original codebase provided the foundation for our enhanced features and capabilities.
COMMUNITY