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🛠️ Memory Journal MCP Server - Documentation Wiki

Welcome to the complete documentation for Memory Journal MCP Server v1.1.3!

🚀 Quick Navigation

Getting Started

• Installation - PyPI, Docker, and source installation
• Quick Start Guide - Get up and running in 2 minutes
• Configuration - Database paths, security, environment variables

Features & Usage

• Tools Reference - Complete guide to all 15 MCP tools
• Prompts Guide - 8 interactive workflow prompts
• Relationship Visualization - Generate Mermaid graphs
• Search Guide - FTS5, date range, and semantic search
• Examples & Tutorials - Real-world usage patterns

Technical Documentation

• Architecture - System design and implementation
• Database Schema - Tables, relationships, and indexes
• Performance - Optimization and best practices
• Security - Input validation, WAL mode, Docker hardening

Advanced Topics

• Git Integration - Auto-context capture
• Semantic Search - ML-powered similarity
• Entry Relationships - Building knowledge graphs
• Data Export - JSON and Markdown formats

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✨ What is Memory Journal?

Memory Journal is an MCP server that captures your development work as structured journal entries with automatic project context. Think of it as a scrapbook for your projects that understands Git, GitHub issues, and the relationships between your work.

Key Features

🛠️ 15 MCP Tools

• Complete CRUD operations
• Triple search system (FTS5 + date + semantic)
• Entry relationships and visualization
• Analytics and goal tracking
• Data export (JSON/Markdown)

🎯 8 Workflow Prompts

• Daily standups
• Sprint retrospectives
• Weekly digests
• Period analysis
• Goal tracking
• Context bundles

🔗 Entry Relationships

• Link entries with typed relationships
• Generate visual Mermaid graphs
• Track how ideas evolve
• Build knowledge graphs

📊 Comprehensive Analytics

• Entry statistics by type
• Tag usage tracking
• Activity patterns (day/week/month)
• Significant milestone tracking

🔍 Triple Search System

1. Full-text - SQLite FTS5 with highlighting
2. Date range - Time-based filtering with tags
3. Semantic - FAISS vector similarity

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🎯 Common Use Cases

Daily Development Work

• Log technical achievements and breakthroughs
• Track bug fixes and debugging sessions
• Document architecture decisions
• Capture "aha!" moments

Team Collaboration

• Prepare daily standups from recent entries
• Generate sprint retrospectives
• Share context bundles with teammates
• Track project milestones

Personal Growth

• Reflect on learning and growth
• Track career milestones
• Document thought processes
• Build a knowledge base

Project Management

• Link related work items
• Visualize project dependencies
• Track goal progress
• Generate weekly summaries

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📚 Documentation Structure

This wiki is organized into logical sections:

1. Getting Started - Installation and configuration
2. Core Features - Tools, prompts, and search
3. Advanced Features - Relationships, visualization, analytics
4. Technical Details - Architecture, schema, performance
5. Examples - Real-world usage patterns

Each page includes code examples, screenshots, and best practices.

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🔗 External Resources

• GitHub Repository - Source code
• Docker Hub - Container images
• PyPI Package - Python package
• MCP Registry - Official listing
• AI Documentation Search - Search all 151 tools across all MCP servers
• Issues & Support - Bug reports

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🚀 Getting Started

Ready to begin? Start with the Installation Guide or jump straight to the Quick Start Guide!

Questions? Check out the Examples page for common patterns and use cases.