mcp-smart-notes

Prototype Model Context Protocol (MCP) note-taking system with intelligent auto-tagging powered by local LLMs. Full MCP specification compliance, JSON-RPC 2.0 transport, and robust error handling with graceful fallbacks.

MCP Smart Note-Taking System

A sophisticated Model Context Protocol (MCP) compliant note-taking system with intelligent automatic tagging capabilities powered by local Large Language Models via Ollama.

Features

• 🏷️ Intelligent Auto-Tagging: Uses LLM analysis to automatically categorize notes
• 📝 Full MCP Compliance: Implements complete MCP specification for seamless AI integration
• 🔍 Advanced Search: Full-text search across titles, content, and tags
• 🗃️ Robust Data Management: JSON-based persistence with atomic operations
• 🛡️ Error Resilience: Multiple fallback mechanisms ensure reliability
• 🎯 Tag Categories: Predefined taxonomy: Greeting, Coding, Education, Finance
• 🤖 Local AI: Privacy-focused with local Ollama integration

🏗️ Architecture

graph TD
    A[Smart Tagging Bridge] --> B[Ollama LLM]
    A --> C[Note Storage]
    A --> D[MCP Protocol]
    D --> E[MCP Server]
    D --> F[MCP Client]
    G[CLI] --> A
    H[Auto-Tagging Engine] --> B
    H --> I[Keyword Fallback]

🚀 Quick Start

Prerequisites

• Python 3.8+
• Ollama installed and running
• Compatible LLM model (recommended: qwen2.5:7b)

Installation

1. Clone the repository

   git clone https://github.com/yourusername/mcp-smart-notes.git
   cd mcp-smart-notes
   

2. Install dependencies

   pip install -r requirements.txt
   

3. Start Ollama (in another terminal)

   ollama serve
   

4. Pull a compatible model

   ollama pull qwen2.5:7b
   

Usage

🎯 Smart Auto-Tagging System (Recommended)

python smart_tagging_bridge.py

💡 Example Usage

You: Create a note about my Python web development project

🔧 Using tool: create_note
🏷️ Auto-assigned tags: ['Coding']
✅ Created note 'Python Web Development Project' with ID: 20250629_223459_661 with tags: ['Coding']

📁 Project Structure

MCP-Testing/
├── 📄 smart_tagging_bridge.py      # Main system with intelligent auto-tagging
├── 📄 note_server.py               # Full MCP server implementation
├── 📄 ollama_mcp_client.py         # MCP client for Ollama integration
├── 📄 simple_note_server.py        # Minimal MCP server for testing
├── 📄 simple_bridge.py             # Direct integration layer
├── 📄 test_auto_tagging.py         # Comprehensive test suite
├── 📄 requirements.txt             # Python dependencies
├── 📄 README.md                    # This file
├── 📄 .gitignore                   # Git ignore patterns
└── 📁 Documentation/     # MCP protocol documentation

🛠️ Available Tools

🏷️ Auto-Tagging Categories

• 🤝 Greeting: Welcomes, introductions, social interactions
• 💻 Coding: Programming, development, technical content
• 📚 Education: Learning, courses, tutorials, studies
• 💰 Finance: Budget, money, investments, financial planning

🔧 Technical Details

MCP Protocol Compliance

• ✅ JSON-RPC 2.0 over stdio transport
• ✅ Tool registration with parameter schemas
• ✅ Resource discovery and URI handling
• ✅ Error handling and status codes
• ✅ Session management and lifecycle

Error Handling

• Ollama Offline: Graceful degradation with helpful messages
• LLM Failures: Automatic keyword-based fallback tagging
• File Corruption: Silent skip of corrupted JSON files
• Network Issues: Retry mechanisms with exponential backoff

🤝 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

📚 Documentation

For detailed MCP protocol information, see:

• Model Context Protocol Specification
• Documentation for Agent/ - Complete MCP documentation

📝 License

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

🙏 Acknowledgments

• Model Context Protocol team for the excellent specification
• Ollama for local LLM capabilities
• Anthropic for Claude's assistance in development

🐛 Known Issues

• Large note collections may experience slower search performance

⭐ Star this repository if you find it useful! ⭐