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.
- 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
- 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
- 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
- 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
- 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
- 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
- Python 3.9 or higher
- Claude Desktop app or compatible MCP client
- API credentials for services you want to integrate
- Clone the repository
git clone https://github.com/vitalune/IPA-mcp.git
cd IPA-mcp- Install dependencies
pip install -r requirements.txt- Configure API credentials
cp config/config.example.yaml config/config.yaml
# Edit config/config.yaml with your API credentials- Initialize the server
python -m src.mainAdd this configuration to your Claude Desktop MCP settings:
{
"mcpServers": {
"personal-knowledge-assistant": {
"command": "python",
"args": ["-m", "src.main"],
"cwd": "/path/to/IPA-mcp"
}
}
}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 |
- Installation Guide - Detailed setup instructions
- API Setup Guide - Configure Gmail, Twitter, LinkedIn, and more
- User Guide - How to use all features effectively
- Configuration Reference - Complete configuration options
- Security Overview - Privacy and security features
- Troubleshooting - Common issues and solutions
- API Reference - Developer documentation
# 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"]Set up your API credentials by following our detailed API Setup Guide:
- 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
Run the comprehensive test suite:
# 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/# 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# 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# 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 suggestionsThe 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
- 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
We welcome contributions! Please see our Contributing Guide for details.
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Make your changes with tests
- Run the test suite (
pytest tests/) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
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.
- Documentation: Check our comprehensive docs
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Security: For security issues, email amirvalizadeh161@gmail.com
Transform your personal knowledge management with AI-powered insights