MCP Server for Deep Research

MCP Server for Deep Research is a powerful tool designed for conducting comprehensive research on complex topics. It helps you explore questions in depth, find relevant sources, and generate structured research reports with proper citations.

🔬 Your personal AI Research Assistant - turning complex research questions into comprehensive, well-cited reports.

✨ What's New

Latest Major Update: Advanced Research Methodology (v0.2.0)

This version introduces a publication-quality research framework with significant depth enhancements:

🎯 Intelligent Complexity Assessment

• Automatically evaluates question complexity (Simple/Moderate/Complex/Highly Complex)

• Dynamically adjusts research depth and methodology based on complexity

• Scales from quick comparisons to comprehensive multi-disciplinary analyses

📊 Multi-Layer Progressive Research

• Layer 1 (Overview): Foundational understanding for all questions

• Layer 2 (Deep Dive): Focused investigation for moderate+ complexity

• Layer 3 (Expert Analysis): Cutting-edge insights for complex topics

🌳 Dynamic Hierarchical Subquestions

• Adaptive quantity: 3-4 questions (simple) → 7-8+ questions (highly complex)

• Tree structure: Core questions with secondary deep-dive sub-questions

• Priority tagging: High/Medium/Low with dependency mapping

🔍 Critical Analysis Framework

• Source Credibility Assessment: Authority, recency, bias evaluation

• Evidence Quality Grading: Strong/Moderate/Weak/Speculative classifications

• Viewpoint Comparison: Mainstream vs. alternative perspectives

• Logical Coherence Checking: Causation vs. correlation, assumption identification

• Hypothesis Testing: Formulate and evaluate testable hypotheses

🛠️ Professional Analysis Frameworks

Choose from 9+ structured methodologies:

• SWOT Analysis (strategic evaluation)

• PEST/PESTEL Analysis (macro-environmental factors)

• 5W2H Framework (diagnostic deep-dive)

• Comparative Analysis (multi-dimensional comparison)

• Trend Analysis (historical → present → future)

• Case Study Method (learn from examples)

• Stakeholder Analysis (perspective mapping)

• Evidence Pyramid (scientific rigor)

• Systems Thinking (interconnections and feedback loops)

🌐 Interdisciplinary Synthesis

• Tags questions with relevant disciplines: Technical, Economic, Social, Ethical, Legal, Scientific, Historical

• Identifies cross-perspective patterns and tensions

• Generates emergent insights from integrated analysis

📄 Publication-Quality Reports

Enhanced structure with:

• Executive Summary (200-300 words)

• Methodology Section (framework justification)

• Critical Analysis (separate from findings)

• Synthesis & Discussion (interdisciplinary integration)

• Confidence Levels (HIGH/MODERATE/LOW/SPECULATIVE)

• Research Limitations (transparent acknowledgment)

• Recommendations (stakeholder-specific actions)

• Further Research Directions

• Glossary (technical terms)

• Supplementary Data (tables, charts)

✅ Enhanced Quality Standards

• Evidence mapping with strength ratings

• Bias and limitation assessment

• Confidence level assignment for all conclusions

• Proper academic-style citations

• Acknowledgment of uncertainty and knowledge boundaries

Core Features (All Versions)

• 🛠️ Direct Tool Access: Call the start_deep_research tool directly from Claude Desktop

• 📊 Structured Research Workflow: Guided process from question elaboration to final report

• 🌐 Web Search Integration: Leverages Claude's built-in search capabilities

• 📝 Professional Reports: Generates well-formatted research reports as artifacts

Related MCP server: MCP Server for Deep Research

🚀 Quick Start

Prerequisites

• Claude Desktop

• Python 3.10 or higher

• uv package manager

Installation

1. Clone this repository

   git clone https://github.com/lihongwen/deepresearch-mcpserver.git
   cd deepresearch-mcpserver

2. Install dependencies

   uv sync

3. Configure Claude Desktop

   Edit your Claude Desktop config file:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

   • Windows: %APPDATA%\Claude\claude_desktop_config.json

   Add the following configuration:

   {
     "mcpServers": {
       "mcp-server-deep-research": {
         "command": "uv",
         "args": [
           "--directory",
           "/path/to/your/deepresearch-mcpserver",
           "run",
           "mcp-server-deep-research"
         ]
       }
     }
   }

4. Restart Claude Desktop

5. Start Researching

   • Use the prompt template: "Start deep research on [your question]"

   • Or call the start_deep_research tool directly

   • Watch as Claude conducts comprehensive research and generates a detailed report

🎯 Complete Research Workflow

The Deep Research MCP Server offers a sophisticated 5-phase research methodology:

Phase 1: Preliminary Analysis & Research Design

• Conceptual Clarification: Defines key terms with precision

• Domain Mapping: Identifies primary knowledge domains and intersections

• Stakeholder Identification: Maps who cares about this question and why

• Complexity Assessment: Evaluates as Simple/Moderate/Complex/Highly Complex

• Strategy Selection: Chooses appropriate analytical frameworks and research depth

Phase 2: Hierarchical Question Decomposition

• Dynamic Subquestion Generation: Creates 3-8 questions based on complexity

• Tree Structure: Core questions with secondary deep-dive sub-questions

• Quality Criteria: Specific, focused, collectively exhaustive, mutually exclusive

• Priority & Dependencies: Tags questions with importance and relationships

• Interdisciplinary Tagging: Labels questions with relevant disciplinary perspectives

Phase 3: Layered Information Gathering

• Layer 1 (Overview): Broad searches, credibility assessment, evidence classification

• Layer 2 (Deep Dive): Focused searches, comparative analysis, pattern identification

• Layer 3 (Expert Analysis): Frontier research, expert discourse, future trajectories

• Source Credibility Ratings: High/Medium/Low based on authority, recency, bias

• Evidence Classification: Strong/Moderate/Weak/Speculative based on rigor

Phase 4: Critical Analysis & Synthesis

• Evidence Mapping: Central claims, supporting/contradicting evidence, gaps

• Logical Coherence Check: Causation vs. correlation, reasoning validity

• Bias Assessment: Selection, confirmation, temporal, publication bias

• Hypothesis Testing: Formulate, evaluate, conclude (Supported/Partial/Not Supported)

• Confidence Levels: Assign HIGH/MODERATE/LOW/SPECULATIVE to conclusions

• Interdisciplinary Synthesis: Cross-perspective patterns, emergent insights, systems understanding

Phase 5: Comprehensive Report Generation

• Executive Summary: 200-300 word standalone overview

• Table of Contents: Auto-generated navigation

• Introduction: Context, importance, scope, key concepts

• Methodology: Complexity rationale, framework selection, limitations

• Findings: Detailed subsections per subquestion with evidence ratings

• Critical Analysis: Evidence strength, contradictions, bias evaluation

• Synthesis & Discussion: Integrated insights, patterns, contextual factors

• Conclusions: Direct answers with confidence levels and implications

• Recommendations: Stakeholder-specific actionable guidance

• Research Limitations: Transparent acknowledgment of constraints

• Further Research: Identified knowledge gaps and future directions

• References: Comprehensive citations with proper formatting

• Appendices: Glossary of terms, supplementary data

💡 Usage Examples

Simple Question

User: "Start deep research on: What is the difference between REST and GraphQL APIs?"

Claude will:
1. Assess as SIMPLE complexity → Layer 1 research only
2. Generate 3-4 focused subquestions (characteristics, use cases, trade-offs)
3. Select Comparative Analysis framework
4. Perform targeted searches with credibility assessment
5. Generate concise report with comparison table

Moderate Question

User: "Start deep research on: What are the applications and challenges of blockchain in supply chain management?"

Claude will:
1. Assess as MODERATE complexity → Layer 1 + Layer 2 research
2. Generate 5-6 core + 2-3 deep-dive subquestions
3. Select SWOT Analysis + Case Study Method + Trend Analysis
4. Perform overview AND focused deep-dive searches
5. Include critical analysis of evidence quality
6. Generate comprehensive report with multiple case studies and confidence levels

Complex Question

User: "Start deep research on: How does climate change impact global food security, and what are effective adaptation strategies?"

Claude will:
1. Assess as COMPLEX → Layer 1 + Layer 2 + Layer 3 research
2. Generate 6-7 core + 3-5 deep-dive subquestions with dependencies
3. Select Systems Thinking + PEST + Stakeholder Analysis + Comparative Analysis
4. Tag with multiple disciplines: Scientific, Economic, Social, Political, Ethical
5. Perform overview + focused + expert-level research
6. Include hypothesis testing (e.g., "Climate-resilient crops maintain yields under 2°C warming")
7. Generate publication-quality report with executive summary, methodology justification, 
   critical analysis, interdisciplinary synthesis, stakeholder recommendations, 
   research limitations, and glossary

🔧 How It Works

1. Call the Tool: Invoke start_deep_research with your research question

2. Follow the Workflow: Claude follows a structured research process

3. Review the Report: Get a comprehensive report as an artifact

4. Cite Sources: All information is properly cited with source URLs

📦 Components

Tools

• start_deep_research: Initiates a comprehensive research workflow on any topic

  • Input: research_question (string)

  • Output: Structured research guidance and workflow

Prompts

• deep-research: Pre-configured prompt template for starting research tasks

Resources

• Dynamic research state tracking

• Progress notes and findings storage

⚙️ Configuration

Claude Desktop Config Locations

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

Development Setup (Local)

{
  "mcpServers": {
    "mcp-server-deep-research": {
      "command": "uv",
      "args": [
        "--directory",
        "C:\\Users\\YourUsername\\path\\to\\deepresearch-mcpserver",
        "run",
        "mcp-server-deep-research"
      ]
    }
  }
}

Production Setup (Published)

If published to PyPI:

{
  "mcpServers": {
    "mcp-server-deep-research": {
      "command": "uvx",
      "args": [
        "mcp-server-deep-research"
      ]
    }
  }
}

🛠️ Development

Setup Development Environment

# Clone the repository
git clone https://github.com/lihongwen/deepresearch-mcpserver.git
cd deepresearch-mcpserver

# Install dependencies
uv sync

# Run in development mode
uv run mcp-server-deep-research

Testing

# Install the MCP Inspector for testing
npx @modelcontextprotocol/inspector uv --directory . run mcp-server-deep-research

Building and Publishing

1. Sync Dependencies

   uv sync

2. Build Distributions

   uv build

   Generates source and wheel distributions in the dist/ directory.

3. Publish to PyPI (if you have publishing rights)

   uv publish

Project Structure

deepresearch-mcpserver/
├── src/
│   └── mcp_server_deep_research/
│       ├── __init__.py
│       └── server.py           # Main MCP server implementation
├── pyproject.toml              # Project configuration
├── README.md                   # This file
└── LICENSE                     # MIT License

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. 🐛 Report Bugs: Open an issue describing the bug

2. 💡 Suggest Features: Share your ideas for improvements

3. 🔧 Submit Pull Requests: Fix bugs or add features

4. 📖 Improve Documentation: Help make the docs better

Contribution Guidelines

• Follow the existing code style

• Add tests for new features

• Update documentation as needed

• Write clear commit messages

📝 Changelog

Version 0.2.0 (Latest) - Major Research Methodology Overhaul

• ✅ Intelligent Complexity Assessment: Automatic evaluation and adaptive methodology

• ✅ Multi-Layer Progressive Research: 3-tier depth system (Overview/Deep-Dive/Expert)

• ✅ Dynamic Hierarchical Subquestions: 3-8 questions based on complexity with tree structure

• ✅ Critical Analysis Framework: Source credibility, evidence grading, bias assessment, hypothesis testing

• ✅ 9+ Analytical Frameworks: SWOT, PEST, 5W2H, Comparative, Trend, Case Study, Stakeholder, Evidence Pyramid, Systems Thinking

• ✅ Interdisciplinary Synthesis: Multi-perspective analysis with cross-domain insights

• ✅ Publication-Quality Reports: Executive summary, methodology, critical analysis, limitations, recommendations, glossary

• ✅ Confidence Level System: HIGH/MODERATE/LOW/SPECULATIVE ratings for all conclusions

• ✅ Enhanced Evidence Standards: Credibility ratings, evidence classification, citation requirements

• ✅ Comprehensive Testing Guide: Test cases for Simple/Moderate/Complex/Highly Complex questions

Version 0.1.0 - Initial Release

• ✅ Added start_deep_research tool for direct invocation

• ✅ Enhanced research workflow with structured prompts

• ✅ Improved error handling and logging

• ✅ Updated documentation with examples

🙏 Acknowledgments

This project is based on the original mcp-server-deep-research by reading-plus-ai.

Special thanks to:

• Anthropic for the MCP protocol and Claude AI

• The open-source community for inspiration and support

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 Links

• Repository: https://github.com/lihongwen/deepresearch-mcpserver

• Issues: https://github.com/lihongwen/deepresearch-mcpserver/issues

• MCP Protocol: https://modelcontextprotocol.io

• Claude Desktop: https://claude.ai/download

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