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vitalune

Personal Knowledge Assistant

by vitalune
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Python
Hybrid

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.

Python 3.9+ MCP Compatible License: MIT Security: Encrypted

🚀 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

git clone https://github.com/vitalune/IPA-mcp.git cd IPA-mcp

  1. Install dependencies

pip install -r requirements.txt

  1. Configure API credentials

cp config/config.example.yaml config/config.yaml # Edit config/config.yaml with your API credentials

  1. Initialize the server

python -m src.main

Connect to Claude Desktop

Add this configuration to your Claude Desktop MCP settings:

{ "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

🔧 Configuration

Basic Configuration

# 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:

  • 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:

# 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

# 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

# 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

# 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 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 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.

🆘 Support

Transform your personal knowledge management with AI-powered insights

Get Started | Documentation | Community

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