Markdown Notes

A powerful command-line interface for managing and searching through markdown notes using advanced semantic search powered by MCP (Model Context Protocol) and machine learning embeddings.

🚀 Features

• Semantic Search: Find notes using natural language queries with AI-powered relevance scoring
• Question-Answering: Ask questions about your notes and get AI-generated answers using Google Gemini
• Note Generation: Create new notes based on prompts or existing content
• Multiple Output Formats: Text, JSON, Table, PDF, HTML, and Markdown exports
• Auto-Tagging: Automatically generate relevant tags for new notes
• MCP Server Integration: Ready for GitHub Copilot integration
• Advanced Statistics: Comprehensive analytics with customizable time periods
• Flexible Export: Export search results in multiple formats with custom filenames
• Robust Error Handling: Graceful fallbacks and comprehensive error recovery
• Batch Operations: Process multiple notes efficiently
• Advanced Search Options: Threshold filtering, export functionality, and custom limits
• Fallback Systems: Multiple fallback implementations for maximum reliability
• Environment Configuration: Customizable settings via environment variables
• Comprehensive Testing: Built-in test scripts for validation

📋 Table of Contents

• Installation
• Quick Start
• Commands
• Configuration
• Examples
• Troubleshooting
• Testing
• Contributing
• License

🔧 Installation

Prerequisites

• Python 3.8 or higher
• Virtual environment (recommended)

Setup

1. Clone or navigate to your project directory:

   cd C:\Users\Devarshi\PycharmProjects\markdown-notes-mcp
   

2. Create and activate virtual environment:

   python -m venv .venv
   .venv\Scripts\activate  # Windows
   # source .venv/bin/activate  # Linux/Mac
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Verify installation:

   python mcp_cli_fixed.py --help
   

🚀 Quick Start

Basic Usage

# Search for notes
python mcp_cli_fixed.py search "machine learning" --limit 5

# Add a new note with auto-tagging
python mcp_cli_fixed.py add-note ./my_note.md --auto-tag

# Export search results as PDF
python mcp_cli_fixed.py export-search "PCA" --format pdf

# View weekly statistics
python mcp_cli_fixed.py stats --period week

# Start MCP server for Copilot integration
python mcp_cli_fixed.py server

# Ask questions about your notes
python mcp_cli_fixed.py qa "Explain PCA from my notes"

# Generate new notes
python mcp_cli_fixed.py generate-note "Web development best practices"

# Generate with reference
python mcp_cli_fixed.py generate-note "Advanced ML" --base-note ml_notes.md

Advanced Usage

# Search with export functionality
python mcp_cli_fixed.py search "cloud computing" --export results.json --format json

# List notes in JSON format (fixed datetime serialization)
python mcp_cli_fixed.py list-notes --format json

# Rebuild index with custom settings
python mcp_cli_fixed.py rebuild-index --chunk-size 300 --overlap 100

# Start server with custom configuration
python mcp_cli_fixed.py server --host 0.0.0.0 --port 8080

📖 Commands

Search Command

Search through your markdown notes using semantic search with advanced options.

python mcp_cli_fixed.py search [OPTIONS] QUERY

Options:

• --format {text,json,table}: Output format (default: text)
• --limit INTEGER: Maximum number of results (default: 10)
• --threshold FLOAT: Minimum relevance threshold (default: 0.0)
• --export PATH: Export results to JSON file

Examples:

# Basic search
python mcp_cli_fixed.py search "machine learning"

# Search with JSON output
python mcp_cli_fixed.py search "python data science" --format json --limit 5

# Search with table format and threshold
python mcp_cli_fixed.py search "algorithms" --format table --threshold 0.8

# Search and export results (NEW!)
python mcp_cli_fixed.py search "cloud computing" --export search_results.json --format json

Add Note Command

Add a new markdown note to your collection with advanced tagging options.

python mcp_cli_fixed.py add-note [OPTIONS] FILE

Options:

• --auto-tag: Automatically generate tags for the note
• --rebuild: Rebuild search index after adding note

Examples:

# Add note without tags
python mcp_cli_fixed.py add-note ./my_note.md

# Add note with auto-tagging
python mcp_cli_fixed.py add-note ./research.md --auto-tag

# Add note and rebuild index
python mcp_cli_fixed.py add-note ./new_findings.md --auto-tag --rebuild

Export Search Command

Export search results in various formats with custom output paths.

python mcp_cli_fixed.py export-search [OPTIONS] QUERY

Options:

• --format {pdf,html,markdown,json}: Export format (default: pdf)
• --output PATH: Output file path
• --limit INTEGER: Maximum results to export (default: 20)

Examples:

# Export as PDF
python mcp_cli_fixed.py export-search "machine learning" --format pdf

# Export as HTML with custom output
python mcp_cli_fixed.py export-search "web development" --format html --output web_guide.html

# Export as Markdown
python mcp_cli_fixed.py export-search "docker" --format markdown --output docker_guide.md

# Export as JSON with limit
python mcp_cli_fixed.py export-search "python" --format json --limit 15 --output python_topics.json

Statistics Command

View comprehensive system statistics with multiple time periods.

python mcp_cli_fixed.py stats [OPTIONS]

Options:

• --period {day,week,month,all}: Time period for statistics (default: week)
• --format {text,json}: Output format (default: text)

Examples:

# Weekly statistics
python mcp_cli_fixed.py stats --period week

# Monthly statistics in JSON
python mcp_cli_fixed.py stats --period month --format json

# Daily statistics
python mcp_cli_fixed.py stats --period day

# All-time statistics
python mcp_cli_fixed.py stats --period all

List Notes Command

List all available notes with metadata and sorting options.

python mcp_cli_fixed.py list-notes [OPTIONS]

Options:

• --sort {name,modified,size}: Sort order (default: name)
• --format {text,json}: Output format (default: text)

Examples:

# List notes sorted by name
python mcp_cli_fixed.py list-notes

# List notes sorted by modification date
python mcp_cli_fixed.py list-notes --sort modified

# List notes sorted by size
python mcp_cli_fixed.py list-notes --sort size

# List notes in JSON format (FIXED: datetime serialization)
python mcp_cli_fixed.py list-notes --format json

Rebuild Index Command

Rebuild the search index with advanced configuration options.

python mcp_cli_fixed.py rebuild-index [OPTIONS] [NOTES_ROOT]

Options:

• --model TEXT: Embedding model to use
• --chunk-size INTEGER: Text chunk size
• --overlap INTEGER: Chunk overlap size
• --force: Force rebuild even if files unchanged

Examples:

# Basic rebuild
python mcp_cli_fixed.py rebuild-index

# Rebuild with different model
python mcp_cli_fixed.py rebuild-index --model all-mpnet-base-v2

# Rebuild with custom chunk settings (FIXED!)
python mcp_cli_fixed.py rebuild-index --chunk-size 300 --overlap 100

# Force rebuild
python mcp_cli_fixed.py rebuild-index --force

# Rebuild specific directory
python mcp_cli_fixed.py rebuild-index ./my_notes

Server Command

Start the MCP server for Copilot integration with improved error handling.

python mcp_cli_fixed.py server [OPTIONS]

Options:

• --host TEXT: Server host (default: 127.0.0.1)
• --port INTEGER: Server port (default: 8181)
• --no-admin: Disable admin HTTP interface

Examples:

# Start server with defaults
python mcp_cli_fixed.py server

# Start server on custom port
python mcp_cli_fixed.py server --port 8080

# Start server on custom host
python mcp_cli_fixed.py server --host 0.0.0.0

# Start server without admin interface
python mcp_cli_fixed.py server --no-admin

# Start server with custom host and port
python mcp_cli_fixed.py server --host 0.0.0.0 --port 9090

QA Command (NEW!)

Ask questions about your notes using AI-powered question answering.

python mcp_cli_fixed.py qa [OPTIONS] QUESTION

Options:

• --limit INTEGER: Maximum context chunks (default: 5)
• --model TEXT: AI model to use (default: gemini-1.5-pro)

Examples:

# Ask a question
python mcp_cli_fixed.py qa "Explain PCA from my notes"

# Ask with more context
python mcp_cli_fixed.py qa "What are machine learning algorithms?" --limit 10

Generate Note Command (NEW!)

Generate new notes based on prompts using AI.

python mcp_cli_fixed.py generate-note [OPTIONS] PROMPT

Options:

• --base-note PATH: Use existing note as reference
• --output PATH: Custom output filename
• --model TEXT: AI model to use (default: gemini-1.5-pro)

Examples:

# Generate a note
python mcp_cli_fixed.py generate-note "Web development best practices"

# Generate with reference
python mcp_cli_fixed.py generate-note "Advanced PCA" --base-note pca_notes.md

# Custom output
python mcp_cli_fixed.py generate-note "ML algorithms" --output ml_guide.md

⚙️ Configuration

Environment Variables

Customize the behavior using environment variables:

# Set embedding model
set MCP_MODEL_NAME=all-mpnet-base-v2

# Set chunk size for text processing
set MCP_CHUNK_SIZE=200

# Set chunk overlap
set MCP_OVERLAP=50

# Set environment
set MCP_ENVIRONMENT=production

Configuration File

The system uses config.py for advanced configuration. You can modify:

• Embedding model settings
• Text chunking parameters
• File paths for index and metadata
• Server configuration

Fallback Systems

The CLI includes multiple fallback systems for maximum reliability:

• MCP Server Fallback: Falls back to simple implementation if full MCP unavailable
• Text Search Fallback: Uses basic text matching if semantic search fails
• Export Fallback: Falls back to text format if PDF/HTML generation fails
• Directory Creation: Automatically creates notes directory if missing

📚 Examples

Complete Workflow

# 1. Setup and check system
python mcp_cli_fixed.py list-notes
python mcp_cli_fixed.py stats

# 2. Add new research notes
echo "# Machine Learning Research
Recent advances in neural networks and deep learning" > research.md
python mcp_cli_fixed.py add-note ./research.md --auto-tag --rebuild

# 3. Search and analyze
python mcp_cli_fixed.py search "neural networks" --format table --limit 5

# 4. Export findings
python mcp_cli_fixed.py export-search "deep learning" --format pdf --output ml_research.pdf

# 5. View statistics
python mcp_cli_fixed.py stats --period week --format json

# 6. Start server for Copilot
python mcp_cli_fixed.py server

Batch Processing

# Add multiple notes
for file in ./new_notes/*.md; do
    python mcp_cli_fixed.py add-note "$file" --auto-tag
done

# Rebuild index after batch
python mcp_cli_fixed.py rebuild-index

# Export multiple searches
python mcp_cli_fixed.py export-search "machine learning" --format pdf --output ml.pdf
python mcp_cli_fixed.py export-search "web development" --format html --output web.html
python mcp_cli_fixed.py export-search "docker" --format markdown --output docker.md

Advanced Search

# High-precision search
python mcp_cli_fixed.py search "machine learning algorithms" --threshold 0.8 --limit 10

# Export search with custom filename
python mcp_cli_fixed.py search "data science" --export ds_results.json --format json

# Search with table output
python mcp_cli_fixed.py search "python libraries" --format table --limit 8

Environment Configuration

# Set custom model
set MCP_MODEL_NAME=all-mpnet-base-v2
set MCP_CHUNK_SIZE=300
set MCP_OVERLAP=100

# Test with new settings
python mcp_cli_fixed.py rebuild-index
python mcp_cli_fixed.py search "machine learning" --limit 5

🔧 Troubleshooting

Common Issues

1. Import Errors:

# Check dependencies
pip install -r requirements.txt

# Verify Python path
python -c "import sys; print(sys.path)"

2. Search Not Working:

# Rebuild index
python mcp_cli_fixed.py rebuild-index

# Check if notes directory exists
dir notes\*.md

3. JSON Serialization Errors (FIXED!):

# This now works automatically
python mcp_cli_fixed.py list-notes --format json

4. Export Functionality (NEW!):

# Export search results
python mcp_cli_fixed.py search "query" --export results.json --format json

5. Server Not Starting:

# Check port availability
netstat -an | findstr :8181

# Try different port
python mcp_cli_fixed.py server --port 8080

6. Rebuild Index Issues (FIXED!):

# Now supports all arguments
python mcp_cli_fixed.py rebuild-index --chunk-size 300 --overlap 100

Debug Mode

Enable debug output for troubleshooting:

# Run with verbose output
python mcp_cli_fixed.py search "test" --limit 3

Recent Fixes

• ✅ JSON datetime serialization - Fixed datetime objects in JSON output
• ✅ Search export functionality - Added --export argument to search command
• ✅ Rebuild-index arguments - Fixed argument parsing and directory handling
• ✅ Server threading issues - Fixed import and threading problems
• ✅ Syntax errors - All f-string and compilation errors fixed
• ✅ Fallback systems - Multiple fallback implementations for reliability

🧪 Testing

Test Scripts

Run comprehensive tests to verify all functionality:

# Run all diagnostic tests
python debug_cli.py
python quick_fix.py
python test_cli_simple.py
python test_cli_fixes.py
python test_all_fixes.py
python final_test.py

Test Individual Components

# Test CLI help
python mcp_cli_fixed.py --help

# Test basic functionality
python mcp_cli_fixed.py list-notes
python mcp_cli_fixed.py stats

# Test search
python mcp_cli_fixed.py search "test" --limit 3

# Test export
python mcp_cli_fixed.py search "test" --export test.json --format json

Test Fallback Systems

# Test without MCP server (uses fallback)
python mcp_cli_fixed.py search "test" --limit 3

# Test with missing dependencies (graceful degradation)
python mcp_cli_fixed.py export-search "test" --format pdf

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes
4. Add tests for new functionality
5. Run tests: python -m pytest
6. Commit your changes: git commit -am 'Add new feature'
7. Push to the branch: git push origin feature-name
8. Submit a pull request

Development Setup

# Clone repository
git clone https://github.com/your-repo/markdown-notes-mcp.git
cd markdown-notes-mcp

# Create development environment
python -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt
pip install -r requirements-dev.txt

# Run tests
python -m pytest

# Run linting
flake8 mcp_cli_fixed.py
black mcp_cli_fixed.py

📄 License

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

🙏 Acknowledgments

• Built with sentence-transformers for semantic search
• Uses FAISS for efficient similarity search
• PDF generation powered by ReportLab
• Table formatting with tabulate
• Robust error handling and fallback systems implemented

📞 Support

For support and questions:

• Create an issue on GitHub
• Check the troubleshooting section above
• Review the examples for common use cases
• Run the test scripts for validation

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Happy note-taking with AI-powered search! 🚀📝

Last updated: September 2025

🆕 Recent Updates

Version 2.1 Features

• 🤖 AI Question-Answering: Ask natural language questions about your notes
• 📝 AI Note Generation: Create new notes based on prompts or existing content
• 🔄 Google Gemini Integration: Advanced AI model for intelligent responses
• ⚙️ Enhanced Configuration: Comprehensive AI settings and model selection
• 🛡️ Robust Error Handling: Automatic retries and fallback systems for AI features
• 📊 Context-Aware Responses: Smart retrieval of relevant note sections
• 🎯 Reference-Based Generation: Use existing notes as context for new content
• ⚡ Performance Optimization: Efficient API usage with rate limit handling