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MCP Deep Web Research Server: Advanced Exploration & Secure Insights - MCP Implementation

MCP Deep Web Research Server: Advanced Exploration & Secure Insights

MCP Deep Web Research Server (v0.3.0): Advanced deep web exploration and data aggregation with enhanced MCP tech for actionable insights, optimized for secure, scalable research workflows.

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43% of users reported increased productivity after just one week

About MCP Deep Web Research Server

What is MCP Deep Web Research Server: Advanced Exploration & Secure Insights?

MCP Deep Web Research Server is an enhanced open-source framework built upon the mcp-webresearch architecture. It empowers users to conduct advanced web exploration and extract secure insights through structured search operations. Built with TypeScript and Playwright, the server provides configurable search workflows, robust error handling, and integration with AI tools like Claude, ensuring compliance with rate limits and network constraints. It serves as a modular research infrastructure for professionals requiring authoritative source validation and iterative investigation.

How to use MCP Deep Web Research Server: Advanced Exploration & Secure Insights?

Implementation involves three core steps: installing dependencies via Playwright, configuring environment variables for concurrency controls, and initiating search workflows through provided APIs. Users interact via CLI commands or integrate the server into existing applications using the documented endpoints. The agentic-research prompt guides AI-driven exploration by prioritizing authoritative sources while maintaining real-time user feedback loops. Logging mechanisms and debug configurations enable troubleshooting through system logs accessible via platform-specific commands.

MCP Deep Web Research Server Features

Key Features of MCP Deep Web Research Server: Advanced Exploration & Secure Insights?

  • Adaptive Search Engine: Implements rate-limiting algorithms with adjustable delays and parallel search caps to prevent API throttling
  • Source Validation: Integrates checksum mechanisms for verifying retrieved data integrity and prioritizing HTTPS sources
  • Configurable Workflows: Environment variables control search depth, concurrency levels, and error retry policies
  • AI-Powered Workflows: Preconfigured prompts enable Claude to autonomously refine search parameters based on intermediate findings
  • Compliance Tracking: Audit logs capture search parameters, source provenance, and user interactions for regulatory reporting

Use Cases for MCP Deep Web Research Server: Advanced Exploration & Secure Insights?

Typical applications include:

  • Competitive intelligence gathering with source credibility scoring
  • Automated compliance audits verifying regulatory documentation across domains
  • Academic research frameworks requiring transparent data provenance tracking
  • Real-time market monitoring with configurable alert thresholds
  • Ethical hacking exercises to assess web application exposure patterns

MCP Deep Web Research Server FAQ

FAQ: MCP Deep Web Research Server

  • Q: What browsers does Playwright support?
    A: Chromium, Firefox, and WebKit with headless execution modes
  • Q: How to optimize performance?
    A: Adjust CONCURRENCY and RATE_DELAY variables based on API provider limits
  • Q: Can I customize search algorithms?
    A: Yes, via API parameters and middleware hooks in the search pipeline
  • Q: How is data secured?
    A: TLS encryption for transport, optional environment variable encryption, and audit logging
  • Q: What contribution guidelines exist?
    A: Follow TypeScript standards, write unit tests, and submit pull requests against develop branch

Content

MCP Deep Web Research Server (v0.3.0)

Node.js Version TypeScript License: MIT

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

Web Research Server MCP server

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

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%\Claude\claude_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 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 integrationdeepwebresearchagentic-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;
        };
      };
    }
    
  1. 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
  1. 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`
  1. Network Timeouts
* Symptom: "Request timed out" error
* Solution: Ensure requests complete within the 60-second MCP timeout
  1. 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
  1. Enable 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

  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

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