Literature Manager

A beginner-friendly system for managing research papers, books, and other sources using AI assistants through the Model Context Protocol (MCP).

🎯 What is this?

This tool helps you:

• Organize research papers, books, websites, and videos
• Take notes on your sources with structured titles
• Track reading progress (unread, reading, completed, archived)
• Connect sources to concepts in your knowledge base
• Work with AI assistants like Claude to manage your literature

🚀 Quick Start

1. Prerequisites

• Python 3.8 or higher

🚀 Quick Start

1. Prerequisites

• Python 3.8 or higher
• Basic familiarity with command line

2. Installation

# Clone this repository
git clone https://github.com/Amruth22/literature-manager-mcp.git
cd literature-manager-mcp

# Install dependencies
pip install -r requirements.txt

# Create your database
python setup_database.py

3. Choose Your Usage Method

Option A: Direct Python Usage (Recommended)

# Set your database path
## 📚 How to Use

### Command Line Interface

```bash
# Add a research paper
python cli.py add-source "Attention Is All You Need" paper arxiv 1706.03762

# Add a book
python cli.py add-source "Deep Learning" book isbn 978-0262035613

# Add a note
python cli.py add-note "Attention Is All You Need" paper arxiv 1706.03762 \
  "Key Insight" "Transformers eliminate recurrence"

# Update status
python cli.py update-status "Attention Is All You Need" paper arxiv 1706.03762 completed

# Link to entity
python cli.py link-entity "Attention Is All You Need" paper arxiv 1706.03762 \
  "transformer architecture" introduces

# List sources
python cli.py list --type paper --status unread

# Search sources
python cli.py search "transformer"

# Show statistics
python cli.py stats

# Get help
python cli.py help

Direct Python Usage

from src.database import LiteratureDatabase

# Initialize database
db = LiteratureDatabase("literature.db")

# Add a source
source_id = db.add_source(
    title="Attention Is All You Need",
    source_type="paper",
    identifier_type="arxiv",
    identifier_value="1706.03762"
# Add a note
db.add_note(source_id, "Key Insight", "Transformers eliminate recurrence...")

# Update status
db.update_status(source_id, "completed")

# Link to entity
db.link_to_entity(source_id, "transformer architecture", "introduces")

# Get source details
source = db.get_source_by_id(source_id)
print(source)

Running Examples

# Run basic examples
python examples/basic_usage.py

# Run advanced examples  
python examples/advanced_usage.py

# Run direct usage examples
python direct_usage.py

• completed: Finished reading
• archived: Saved for later reference

🔗 Relationship Types

When linking sources to concepts:

• discusses: Source talks about the concept
• introduces: Source first presents the concept
• extends: Source builds upon the concept
• evaluates: Source analyzes/critiques the concept
• applies: Source uses the concept practically
• critiques: Source criticizes the concept

🛠️ Available Commands

Basic Operations

• add_source() - Add a new source
• add_note() - Add notes to sources
• update_status() - Change reading status
• search_sources() - Find sources

Advanced Operations

• link_to_entity() - Connect sources to concepts
• get_entity_sources() - Find sources by concept
• add_identifier() - Add more IDs to existing sources

Database Operations

• list_sources() - Show all sources
• get_source_details() - Get complete source info
• database_stats() - Show database statistics

📁 Project Structure

literature-manager-mcp/
├── README.md              # This file
├── requirements.txt       # Python dependencies
├── setup_database.py      # Database setup script
├── server.py             # Main MCP server
├── src/
│   ├── __init__.py
│   ├── database.py       # Database operations
│   ├── models.py         # Data models
│   ├── tools.py          # MCP tools
│   └── utils.py          # Helper functions
├── examples/
│   ├── basic_usage.py    # Simple examples
│   └── advanced_usage.py # Complex workflows
├── tests/
│   └── test_basic.py     # Unit tests
└── docs/
    ├── installation.md   # Detailed setup
    ├── examples.md       # More examples
    └── troubleshooting.md # Common issues

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

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

🆘 Need Help?

• Check the troubleshooting guide
• Look at examples
• Open an issue

🙏 Acknowledgments

• Based on the original work by zongmin-yu
• Built with FastMCP
• Uses Model Context Protocol