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. Weβre 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
npmandnpx) - Claude Desktop app
Installation
Global Installation (Recommended)
# 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
Local Project Installation
# Using npm
npm install mcp-deepwebresearch
# Using yarn
yarn add mcp-deepwebresearch
# Using pnpm
pnpm add mcp-deepwebresearch
Claude Desktop Integration
After installing the package, add this entry to your claude_desktop_config.json:
Windows
{
"mcpServers": {
"deepwebresearch": {
"command": "mcp-deepwebresearch",
"args": []
}
}
}
Location: %APPDATA%Claudeclaude_desktop_config.json
macOS
{
"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.
First-time Setup
After installation, run this command to install required browser dependencies:
npx playwright install chromium
Usage
Simply start a chat with Claude and send a prompt that would benefit from web research. If youβd 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 β deepwebresearch β agentic-research.
Tools
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 }
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: βinfoβ)
Error Handling
Common Issues
Rate Limiting
- Symptom: βToo many requestsβ error
- Solution: Increase
SEARCH_DELAY_MSor decreaseMAX_PARALLEL_SEARCHES
Network Timeouts
- Symptom: βRequest timed outβ error
- Solution: Ensure requests complete within the 60-second MCP timeout
Browser Issues
- Symptom: βBrowser failed to launchβ error
- Solution: Ensure Playwright is properly installed (
npx playwright install)
Debugging
This is beta software. If you run into issues:
Check Claude Desktopβs MCP logs:
# On macOS tail -n 20 -f ~/Library/Logs/Claude/mcp*.log # On Windows Get-Content -Path "$env:APPDATAClaudelogsmcp*.log" -Tail 20 -WaitEnable debug logging:
export LOG_LEVEL=debug
Development
Setup
# Install dependencies
pnpm install
# Build the project
pnpm build
# Watch for changes
pnpm watch
# Run in development mode
pnpm dev
Testing
# Run all tests
pnpm test
# Run tests in watch mode
pnpm test:watch
# Run tests with coverage
pnpm test:coverage
Code Quality
# Run linter
pnpm lint
# Fix linting issues
pnpm lint:fix
# Type check
pnpm type-check
Contributing
- 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
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
- [x] macOS
- [x] 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
DEEP Web Research
Project Details
- qpd-v/mcp-DEEPwebresearch
- mcp-deepwebresearch
- MIT License
- Last Updated: 4/18/2025
Categories
Recomended MCP Servers
HTTP-4-MCP configuration tool allows you to easily convert HTTP API to MCP tool without writing code. With simple...
An MCP server that provides LLMs with the latest stable package versions when coding
A Nostr MCP server that allows to interact with Nostr, enabling posting notes, and more.
Provide latest cryptocurrency news to AI agents.
MCP Server for Trino
π¨βπ§ δΊΊιγMCPγ΅γΌγγΌγ¨γγ¦ε©η¨γγπ©βπ§
Model Context Protocol (MCP) server that interacts with a Language Server
Provides summarised output from various actions that could otherwise eat up tokens and cause crashes for AI agents
Model Context Protocol (MCP) Server for Apify's Actors
mcp server for tidb





