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MCP Web Research Server

by qpd-v
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TypeScript
Hybrid

MCP Deep Web Research Server (v0.3.0)

Node.js Version TypeScript License: MIT

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.

Related MCP server: MCP Web Research Server

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 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

# 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

Available Tools

3 tools
deep_researchC

Perform deep research on a topic with content extraction and analysis

ParametersJSON Schema
NameRequiredDescriptionDefault
maxBranchingNoMaximum number of related paths to explore
maxDepthNoMaximum depth of related content exploration
minRelevanceScoreNoMinimum relevance score for including content
timeoutNoResearch timeout in milliseconds
topicYesResearch topic or question

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'content extraction and analysis' but fails to detail critical aspects such as execution time, resource usage, error handling, or output format. This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and avoids redundancy, making it highly concise and well-structured for quick comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a 'deep research' tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'deep research' entails, how results are returned, or any behavioral constraints, leaving the agent with inadequate information for effective use in a broader context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, meaning all parameters are documented in the schema. The description adds no additional semantic context about parameters beyond implying 'deep research' involves branching and depth. This meets the baseline for high schema coverage but doesn't enhance understanding of parameter roles or interactions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Perform deep research on a topic with content extraction and analysis,' which specifies the verb (perform deep research) and resource (topic) with additional capabilities (content extraction and analysis). However, it doesn't explicitly differentiate from sibling tools like 'parallel_search' or 'visit_page,' which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'parallel_search' or 'visit_page.' It lacks any context about appropriate scenarios, prerequisites, or exclusions, leaving the agent with minimal direction for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

visit_pageC

Visit a webpage and extract its content

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesURL to visit

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'visit a webpage and extract its content', which implies a read operation, but doesn't specify details like authentication needs, rate limits, error handling, or what 'extract content' entails (e.g., HTML, text, metadata). For a tool with no annotations, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core action ('visit a webpage') and purpose ('extract its content'), making it easy to understand quickly. Every part of the sentence earns its place by conveying essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (a web interaction tool with potential behavioral nuances) and the lack of annotations and output schema, the description is incomplete. It doesn't cover what 'extract content' means in terms of output format, error cases, or limitations. For a tool that interacts with external webpages, more context is needed to ensure proper usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the 'url' parameter clearly documented as 'URL to visit'. The description adds no additional meaning beyond this, as it doesn't elaborate on URL format constraints or extraction specifics. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to given the schema's clarity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('visit') and resource ('webpage'), and specifies the action ('extract its content'). However, it doesn't differentiate this tool from potential sibling tools like 'deep_research' or 'parallel_search', which might have overlapping functionality. The description is not tautological but lacks sibling distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, and doesn't reference sibling tools like 'deep_research' or 'parallel_search' that might be related. Usage is implied only by the tool's name and description, with no explicit guidelines.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: deep_research is for comprehensive topic analysis, parallel_search is for multi-query search execution, and visit_page is for single-page content extraction. There is no overlap in functionality, making tool selection unambiguous for an agent.

Naming Consistency5/5

All tools follow a consistent snake_case verb_noun pattern (deep_research, parallel_search, visit_page) with clear action-oriented names. The naming scheme is predictable and readable throughout the set.

Tool Count3/5

With only 3 tools, the set feels thin for a 'Web Research Server' domain, lacking operations like search filtering, result summarization, or citation management. While the tools cover core actions, the count is borderline minimal for comprehensive research workflows.

Completeness3/5

The tools cover basic research steps (search, page access, analysis), but there are notable gaps: no ability to refine searches, save results, compare sources, or handle authentication. This limits agents to a linear workflow without advanced research capabilities.

Maintenance

ActivityInactive
ResponsivenessNo issues

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