Deep Research MCP Server

# 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](https://claude.ai/download) - Python 3.10 or higher - `uv` package manager ### Installation 1. **Clone this repository** ```bash git clone https://github.com/lihongwen/deepresearch-mcpserver.git cd deepresearch-mcpserver ``` 2. **Install dependencies** ```bash 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: ```json { "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) ```json { "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: ```json { "mcpServers": { "mcp-server-deep-research": { "command": "uvx", "args": [ "mcp-server-deep-research" ] } } } ``` ## 🛠️ Development ### Setup Development Environment ```bash # 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 ```bash # Install the MCP Inspector for testing npx @modelcontextprotocol/inspector uv --directory . run mcp-server-deep-research ``` ### Building and Publishing 1. **Sync Dependencies** ```bash uv sync ``` 2. **Build Distributions** ```bash uv build ``` Generates source and wheel distributions in the `dist/` directory. 3. **Publish to PyPI** (if you have publishing rights) ```bash 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](https://github.com/reading-plus-ai/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](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**