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

🚀 Quick Start

Prerequisites

Claude Desktop
Python 3.10 or higher
uv package manager

Installation

Clone this repository

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

Install dependencies
```
uv sync
```

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"
      ]
    }
  }
}

Restart Claude Desktop
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

Call the Tool: Invoke start_deep_research with your research question
Follow the Workflow: Claude follows a structured research process
Review the Report: Get a comprehensive report as an artifact
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

Sync Dependencies
```
uv sync
```
Build Distributions
```
uv build
```
Generates source and wheel distributions in the dist/ directory.
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:

🐛 Report Bugs: Open an issue describing the bug
💡 Suggest Features: Share your ideas for improvements
🔧 Submit Pull Requests: Fix bugs or add features
📖 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

Made with ❤️ for better AI-powered research

deepresearch-mcpserver