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🛠️ Memory Journal MCP Server - Documentation Wiki

Welcome to the complete documentation for Memory Journal MCP Server v3.1.2!

🎉 New in v3.0.0:

  • Complete TypeScript Rewrite - Pure JS stack with zero native dependencies
  • Backup & Restore Tools - backup_journal, list_backups, restore_backup
  • GitHub Projects v2 Kanban - get_kanban_board, move_kanban_item tools
  • Server Health Resource - memory://health for diagnostics
  • Tool Annotations - MCP 2025-11-25 compliant behavioral hints
  • Full Release Notes →

Installation (Node.js 18+):

npm install -g memory-journal-mcp

🚀 Quick Navigation

Getting Started

Features & Usage

Technical Documentation

Advanced Topics

Release Notes & Changelog

  • Changelog - Complete version history and release notes

✨ What is Memory Journal?

Memory Journal is a project context management system designed to solve the fragmented AI context problem in large-scale development. When working with AI assistance across multiple threads and sessions, context is constantly lost. Memory Journal maintains a persistent, searchable knowledge graph of your project work, decisions, and progress - ensuring every AI conversation has access to your complete development history.

Think of it as: Long-term memory for AI-assisted development, bridging the gap between disconnected threads and enabling context continuity across your entire project lifecycle.

Key Features

🛠️ 29 MCP Tools

  • Complete CRUD operations
  • Triple search system (FTS5 + date + semantic)
  • Entry relationships and visualization
  • Analytics and goal tracking
  • Backup & Restore (v3.0.0)
  • GitHub Projects v2 Kanban (v3.0.0+)
  • Cross-project insights
  • Data export (JSON/Markdown)

🎯 14 Workflow Prompts

  • Daily standups
  • Sprint retrospectives
  • Weekly digests
  • Period analysis
  • Goal tracking
  • Context bundles
  • Project status summaries
  • Milestone tracking
  • PR summary with journal activity
  • Code review preparation
  • PR retrospective analysis

🔗 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 - vectra vector similarity (included by default)

🎯 Common Use Cases

AI-Assisted Development (Primary Use Case)

  • Context Continuity - Provide AI with complete project history across threads
  • Decision Recall - Query past architectural decisions and their rationale
  • Pattern Recognition - Find similar problems and solutions from earlier work
  • Progress Tracking - Show AI your project timeline and current status

Development Teams & Project Management

  • Thread Handoffs - Transfer context between team members working with AI
  • Sprint Planning - Generate comprehensive project status from accumulated history
  • Milestone Tracking - Monitor progress across fragmented work sessions
  • Knowledge Retention - Build institutional memory as team composition changes

Solo Developers

  • Context Recovery - Resume work after breaks with full project context
  • Architecture Documentation - Maintain living record of technical decisions
  • Progress Visualization - See how implementations evolve from initial designs
  • Debugging History - Track what you've tried when troubleshooting

Cross-Project Work

  • Multi-Project Context - Maintain separate knowledge graphs per project
  • Pattern Transfer - Find solutions from one project applicable to another
  • Time Allocation - Analyze where development effort is concentrated
  • Team Coordination - Share project context across organizational boundaries

📚 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.

🔗 External Resources

🚀 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.

🏠 Home

Getting Started

Core Features

Knowledge Graph

GitHub Integration

Workflows

Deployment

Architecture

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