MCP Deep Web Research Server

MCP Deep Web Research Server (v0.3.0)

A Model Context Protocol (MCP) server for advanced web research.

Latest Changes

• 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.

Features

• 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

Prerequisites

• Node.js >= 18 (includes npm and npx)
• Claude Desktop app

Installation

Installing via Smithery

To install Deep Web Research Server for Claude Desktop automatically via Smithery:

Global Installation (Recommended)

Local Project Installation

Claude Desktop Integration

After installing the package, add this entry to your claude_desktop_config.json:

Windows

Location: %APPDATA%\Claude\claude_desktop_config.json

macOS

Location: ~/Library/Application Support/Claude/claude_desktop_config.json

This config allows Claude Desktop to automatically start the web research MCP server when needed.

First-time Setup

After installation, run this command to install required browser dependencies:

Usage

Simply 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.

Tools

1. 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; }; }; }
2. 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
3. visit_page
   • Visit a webpage and extract its content
   • Arguments: { url: string }
   • Returns:
     { url: string; title: string; content: string; // Markdown formatted content }

Prompts

agentic-research

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

Configuration Options

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')

Error Handling

Common Issues

1. Rate Limiting
   • Symptom: "Too many requests" error
   • Solution: Increase SEARCH_DELAY_MS or decrease MAX_PARALLEL_SEARCHES
2. Network Timeouts
   • Symptom: "Request timed out" error
   • Solution: Ensure requests complete within the 60-second MCP timeout
3. Browser Issues
   • Symptom: "Browser failed to launch" error
   • Solution: Ensure Playwright is properly installed (npx playwright install)

Debugging

This is beta software. If you run into issues:

1. 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
2. Enable debug logging:
   export LOG_LEVEL=debug

Development

Setup

Testing

Code Quality

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Coding Standards

• Follow TypeScript best practices
• Maintain test coverage above 80%
• Document new features and APIs
• Update CHANGELOG.md for significant changes
• Follow semantic versioning

Performance Considerations

• 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

Requirements

• Node.js >= 18
• Playwright (automatically installed as a dependency)

Verified Platforms

• macOS
• Windows
• Linux

License

MIT

Credits

This project builds upon the excellent work of mcp-webresearch by mzxrai. The original codebase provided the foundation for our enhanced features and capabilities.

Author

qpd-v