Personal Knowledge Assistant

# Personal Knowledge Assistant MCP Server A comprehensive Model Context Protocol (MCP) server that transforms how you manage and analyze your personal information across email, social media, documents, and productivity metrics. [](https://www.python.org/downloads/) [](https://github.com/anthropic/mcp) [](https://opensource.org/licenses/MIT) [](docs/SECURITY.md) ## 🚀 Features ### 📧 **Email Intelligence** - **Smart Email Management**: Search, analyze, and compose emails with AI assistance - **Communication Pattern Analysis**: Understand your email habits, response times, and relationship dynamics - **Thread Analysis**: Track email conversations and extract actionable insights - **Automated Categorization**: Intelligent labeling and organization of your inbox ### 🌐 **Social Media Integration** - **Multi-Platform Support**: Twitter, LinkedIn, Facebook, and Instagram - **Content Performance Analysis**: Track engagement metrics, reach, and audience insights - **Optimal Timing**: AI-powered recommendations for when to post - **Cross-Platform Publishing**: Post to multiple platforms simultaneously ### 📁 **Document Management** - **Universal Search**: Find documents across Google Drive, Dropbox, and local files - **Content Analysis**: Extract key insights and summaries from documents - **Version Tracking**: Monitor document changes and collaboration patterns - **Smart Organization**: Automatic tagging and categorization ### 📊 **Personal Analytics** - **Productivity Metrics**: Track work patterns, focus time, and task completion - **Habit Monitoring**: Build and maintain positive habits with data-driven insights - **Goal Progress**: Monitor and analyze progress toward personal and professional goals - **Health & Wellness**: Integrate mood, energy, and wellness tracking ### 🧠 **AI-Powered Insights** - **Behavioral Pattern Detection**: Identify trends in your communication and work habits - **Predictive Analytics**: Anticipate busy periods and optimize your schedule - **Relationship Mapping**: Visualize your professional and personal networks - **Automated Reports**: Daily, weekly, and monthly insight summaries ### 🔒 **Privacy & Security** - **End-to-End Encryption**: All data encrypted at rest and in transit - **Local Processing**: Sensitive analysis performed locally when possible - **GDPR Compliant**: Full data export and deletion capabilities - **Audit Logging**: Complete audit trail of all data access and processing ## 🎯 Quick Start ### Prerequisites - Python 3.9 or higher - Claude Desktop app or compatible MCP client - API credentials for services you want to integrate ### Installation 1. **Clone the repository** ```bash git clone https://github.com/vitalune/IPA-mcp.git cd IPA-mcp ``` 2. **Install dependencies** ```bash pip install -r requirements.txt ``` 3. **Configure API credentials** ```bash cp config/config.example.yaml config/config.yaml # Edit config/config.yaml with your API credentials ``` 4. **Initialize the server** ```bash python -m src.main ``` ### Connect to Claude Desktop Add this configuration to your Claude Desktop MCP settings: ```json { "mcpServers": { "personal-knowledge-assistant": { "command": "python", "args": ["-m", "src.main"], "cwd": "/path/to/IPA-mcp" } } } ``` ## 🛠️ Available Tools The server provides 7 powerful MCP tools: | Tool | Description | Key Features | |------|-------------|--------------| | `send_email` | Compose and send emails with AI assistance | Smart composition, attachment support, multiple recipients | | `analyze_email_patterns` | Analyze communication patterns and relationships | Response times, frequency analysis, sentiment tracking | | `post_social_media` | Create and schedule social media posts | Multi-platform, optimal timing, hashtag suggestions | | `analyze_social_engagement` | Track social media performance and insights | Engagement metrics, audience analysis, trend identification | | `manage_project_context` | Organize projects, tasks, and deadlines | Intelligent prioritization, timeline tracking, team collaboration | | `track_personal_metrics` | Monitor productivity, habits, and goals | Custom metrics, trend analysis, achievement tracking | | `generate_insights_report` | Create comprehensive analytics reports | Multi-source data, actionable recommendations, export options | ## 📖 Documentation - [**Installation Guide**](docs/INSTALL.md) - Detailed setup instructions - [**API Setup Guide**](docs/API_SETUP.md) - Configure Gmail, Twitter, LinkedIn, and more - [**User Guide**](docs/USER_GUIDE.md) - How to use all features effectively - [**Configuration Reference**](docs/CONFIGURATION.md) - Complete configuration options - [**Security Overview**](docs/SECURITY.md) - Privacy and security features - [**Troubleshooting**](docs/TROUBLESHOOTING.md) - Common issues and solutions - [**API Reference**](docs/API_REFERENCE.md) - Developer documentation ## 🔧 Configuration ### Basic Configuration ```yaml # config/config.yaml app: name: "Personal Knowledge Assistant" environment: "development" security: encryption_enabled: true session_timeout_minutes: 60 require_mfa: false privacy: anonymize_logs: true data_retention_days: 90 enable_analytics: false integrations: gmail: enabled: true scopes: ["gmail.readonly", "gmail.send"] twitter: enabled: true scopes: ["tweet.read", "tweet.write"] linkedin: enabled: true scopes: ["r_liteprofile", "w_member_social"] ``` ### API Credentials Set up your API credentials by following our detailed [API Setup Guide](docs/API_SETUP.md): - **Gmail/Google Drive**: Google Cloud Console OAuth 2.0 - **Twitter**: Twitter Developer Portal API keys - **LinkedIn**: LinkedIn Developer Program credentials - **Other Services**: Platform-specific setup instructions ## 🧪 Testing Run the comprehensive test suite: ```bash # Install test dependencies pip install -r tests/requirements.txt # Run all tests pytest tests/ # Run specific test categories pytest tests/unit/ # Unit tests pytest tests/integration/ # Integration tests pytest tests/security/ # Security tests pytest tests/mcp/ # MCP protocol compliance # Generate coverage report pytest --cov=src tests/ ``` ## 🚀 Example Usage ### Analyze Your Email Patterns ```python # Ask Claude: "Analyze my email communication patterns for the last month" # The MCP server will: # 1. Fetch emails from the specified timeframe # 2. Analyze response times, frequency, and relationships # 3. Generate insights about your communication habits # 4. Provide actionable recommendations ``` ### Cross-Platform Social Media Management ```python # Ask Claude: "Post about our product launch to Twitter and LinkedIn, optimized for engagement" # The MCP server will: # 1. Analyze your audience and engagement patterns # 2. Suggest optimal posting times # 3. Craft platform-appropriate content # 4. Schedule posts across multiple platforms ``` ### Comprehensive Productivity Analysis ```python # Ask Claude: "Generate a weekly productivity report with insights and recommendations" # The MCP server will: # 1. Aggregate data from emails, calendar, and personal metrics # 2. Identify productivity patterns and bottlenecks # 3. Compare with previous periods # 4. Provide personalized improvement suggestions ``` ## 🏗️ Architecture The Personal Knowledge Assistant is built with a modular, secure architecture: ``` ├── src/ │ ├── main.py # MCP server entry point │ ├── tools/ # MCP tool implementations │ ├── integrations/ # API client implementations │ ├── utils/ # Analytics, NLP, and utilities │ ├── models/ # Data models and schemas │ └── config/ # Configuration and authentication ├── tests/ # Comprehensive test suite ├── docs/ # Documentation └── config/ # Configuration templates ``` ### Key Components - **MCP Protocol Layer**: Standards-compliant MCP server implementation - **API Integration Layer**: Secure, rate-limited connections to external services - **Analytics Engine**: Advanced data processing and insight generation - **Security Layer**: Encryption, authentication, and privacy controls - **Storage Layer**: Secure local data storage with optional cloud sync ## 🤝 Contributing We welcome contributions! Please see our [Contributing Guide](docs/CONTRIBUTING.md) for details. 1. Fork the repository 2. Create a feature branch (`git checkout -b feature/amazing-feature`) 3. Make your changes with tests 4. Run the test suite (`pytest tests/`) 5. Commit your changes (`git commit -m 'Add amazing feature'`) 6. Push to the branch (`git push origin feature/amazing-feature`) 7. Open a Pull Request ## 📄 License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## ⚠️ Security & Privacy Your privacy is our priority: - **Local-First**: Sensitive processing happens on your machine - **Encrypted Storage**: All data encrypted using industry-standard algorithms - **Minimal Data Collection**: We only collect what's necessary for functionality - **Transparent Logging**: Complete audit trail of all data access - **User Control**: Full data export and deletion capabilities For more details, see our [Security Documentation](docs/SECURITY.md). ## 🆘 Support - **Documentation**: Check our [comprehensive docs](docs/) - **Issues**: [GitHub Issues](https://github.com/vitalune/IPA-mcp/issues) - **Discussions**: [GitHub Discussions](https://github.com/vitalune/IPA-mcp/discussions) - **Security**: For security issues, email amirvalizadeh161@gmail.com --- <div align="center"> **Transform your personal knowledge management with AI-powered insights** [Get Started](docs/INSTALL.md) | [Documentation](docs/) | [Community](https://github.com/vitalune/discussions) </div>