memory-journal-mcp 1.1.3

A Model Context Protocol server for personal journaling with context awareness, relationships, and visualization

🛠️ Memory Journal MCP Server

Last Updated October 8, 2025 3:50 PM EST

A production-ready developer journal with knowledge graphs, visual relationship mapping, and intelligent search

🎉 Now Production/Stable! Memory Journal has graduated from beta with powerful relationship mapping, 10x faster startup, and comprehensive documentation.

🚀 Quick Deploy:

• PyPI Package - pip install memory-journal-mcp
  • Docker Hub - Alpine-based (225MB) with full semantic search
• MCP Registry - Discoverable by MCP clients

📚 Full Documentation: GitHub Wiki

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✨ What's New in v1.1.3

🎉 Production/Stable Release

Memory Journal has officially graduated from beta! This release includes:

• 15 MCP tools (up from 13)
• 8 workflow prompts (up from 6)
• 3 MCP resources (up from 2)
• 17 comprehensive wiki pages
• Automatic schema migrations
• Production-grade stability

🔗 Entry Relationships & Knowledge Graphs

Build connections between your entries with typed relationships:

• references - General connections between work
• implements - Link implementations to specs/designs
• clarifies - Add explanations and elaborations
• evolves_from - Track how ideas develop over time
• response_to - Thread conversations and replies

📊 Visual Relationship Mapping

Generate beautiful Mermaid diagrams showing how your work connects:

graph TD
    E55["#55: Implementing visualization feature<br/>development_note"]
    E56["#56: Testing the new tool<br/>technical_note"]
    E57["#57: Documentation improvements<br/>enhancement"]
    
    E56 ==>|implements| E55
    E57 -.->|clarifies| E55
    
    style E55 fill:#FFF3E0
    style E56 fill:#FFF3E0
    style E57 fill:#FFF3E0

⚡ Performance Revolution

• 10x faster startup - Lazy loading reduces init time from 14s → 2-3s
• Thread-safe operations - Zero race conditions in concurrent tag creation
• Database lock prevention - Single-connection transactions eliminate conflicts
• Optimized queries - Strategic indexes for relationship traversal

🛠️ New Tools (15 Total, +2 from v1.0)

• visualize_relationships - Generate Mermaid diagrams with depth control
• link_entries - Create typed relationships between entries
• Plus comprehensive CRUD, triple search, analytics, and export

🎯 Enhanced Workflow Prompts (8 Total, +2 from v1.0)

• find-related - Discover connected entries via semantic similarity
• prepare-standup - Daily standup summaries
• prepare-retro - Sprint retrospectives
• weekly-digest - Day-by-day weekly summaries
• analyze-period - Deep period analysis with insights
• goal-tracker - Milestone and achievement tracking
• get-context-bundle - Project context with Git/GitHub
• get-recent-entries - Formatted recent entries

📡 New Resources (3 Total, +1 from v1.0)

• memory://graph/recent - NEW Live Mermaid diagram of recent relationships
• memory://recent - 10 most recent entries
• memory://significant - Significant milestones and breakthroughs

🗄️ Database Improvements

• Automatic schema migrations (seamless v1.0 → v1.1 upgrades)
• Soft delete support with deleted_at column
• New relationships table with cascading deletes
• Enhanced indexes for optimal query performance

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🚀 Quick Start

Option 1: PyPI (Fastest - 30 seconds)

# Install
pip install memory-journal-mcp

# Add to ~/.cursor/mcp.json
{
  "mcpServers": {
    "memory-journal": {
      "command": "memory-journal-mcp"
    }
  }
}

# Restart Cursor → Start journaling!

Option 2: Docker (Full Features - 2 minutes)

# Pull image
docker pull writenotenow/memory-journal-mcp:latest

# Create data directory
mkdir data

# Add to ~/.cursor/mcp.json
{
  "mcpServers": {
    "memory-journal": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i", 
        "-v", "./data:/app/data",
        "writenotenow/memory-journal-mcp:latest",
        "python", "src/server.py"
      ]
    }
  }
}

# Restart Cursor → Start journaling!

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📋 Core Capabilities

🛠️ 15 MCP Tools - Complete Development Workflow

Entry Management:

• create_entry / create_entry_minimal - Create entries with auto-context
• update_entry - Edit existing entries (thread-safe)
• delete_entry - Soft or permanent deletion
• get_entry_by_id - Retrieve with full relationship details

Search & Discovery:

• search_entries - FTS5 full-text search with highlighting
• search_by_date_range - Time-based filtering with tags
• semantic_search - ML-powered similarity (optional)
• get_recent_entries - Quick access to recent work

Relationships & Visualization:

• link_entries - Create typed relationships
• visualize_relationships - Generate Mermaid diagrams

Organization & Analytics:

• list_tags - Tag usage statistics
• get_statistics - Comprehensive analytics by time period
• export_entries - JSON/Markdown export
• test_simple - Connectivity testing

🎯 8 Workflow Prompts - Automated Productivity

• prepare-standup - Daily standup summaries from recent entries
• prepare-retro - Sprint retrospectives with achievements and learnings
• weekly-digest - Day-by-day weekly summaries
• analyze-period - Deep analysis with pattern insights
• goal-tracker - Milestone and achievement tracking
• find-related - Discover connected entries via semantic similarity
• get-context-bundle - Complete project context (Git + GitHub)
• get-recent-entries - Formatted display of recent work

🔍 Triple Search System - Find Anything, Any Way

1. Full-text search - SQLite FTS5 with result highlighting and rank ordering
2. Date range search - Time-based filtering with tag and type filters
3. Semantic search - FAISS vector similarity for concept-based discovery (optional)

🔗 Entry Relationships - Build Your Knowledge Graph

• 5 relationship types - references, implements, clarifies, evolves_from, response_to
• Bidirectional linking - See both incoming and outgoing relationships
• Graph visualization - Generate Mermaid diagrams with depth control
• Smart discovery - Find related entries via semantic similarity and shared tags

📊 Comprehensive Analytics - Track Your Progress

• Entry counts by type (achievements, notes, milestones, etc.)
• Top tags with usage statistics
• Activity patterns by day/week/month
• Significant milestone tracking
• Export-ready statistics for reports

🎨 Visual Relationship Graphs - See How Work Connects

• 3 visualization modes - Entry-centric, tag-based, recent activity
• Customizable depth - Control relationship traversal (1-3 hops)
• Tag filtering - Focus on specific projects or topics
• Color-coded nodes - Personal (blue) vs Project (orange) entries
• Typed arrows - Different styles for different relationship types

🔄 Git & GitHub Integration - Automatic Context Capture

• Repository name and path
• Current branch
• Latest commit (hash + message)
• Recent GitHub issues (via gh CLI)
• Working directory
• Timestamp for all context

📦 Data Export - Own Your Data

• JSON format - Machine-readable with full metadata
• Markdown format - Human-readable with beautiful formatting
• Flexible filtering - By date range, tags, entry types
• Portable - Take your journal anywhere

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📖 Usage Examples

Create an Entry with Relationships

// Create a technical achievement
create_entry({
  content: "Implemented lazy loading for ML dependencies - 10x faster startup!",
  entry_type: "technical_achievement",
  tags: ["performance", "optimization", "ml"],
  significance_type: "technical_breakthrough"
})
// Returns: Entry #55

// Link related work
link_entries({
  from_entry_id: 56,  // Testing entry
  to_entry_id: 55,    // Implementation
  relationship_type: "implements"
})

// Visualize the connections
visualize_relationships({
  entry_id: 55,
  depth: 2
})

Search and Analyze

// Full-text search with highlighting
search_entries({ query: "performance optimization", limit: 5 })

// Semantic search for concepts
semantic_search({ query: "startup time improvements", limit: 3 })

// Date range with tags
search_by_date_range({
  start_date: "2025-10-01",
  end_date: "2025-10-31",
  tags: ["performance"]
})

// Get analytics
get_statistics({ group_by: "week" })

Generate Visual Maps

// Visualize entry relationships
visualize_relationships({
  entry_id: 55,  // Root entry
  depth: 2       // 2 hops out
})

// Filter by tags
visualize_relationships({
  tags: ["visualization", "relationships"],
  limit: 20
})

// Access live graph resource
memory://graph/recent  // Most recent 20 entries with relationships

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🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│ MCP Server Layer (Async/Await)                              │
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────┐  │
│  │ Entry Creation  │  │ Triple Search   │  │ Relationship│  │
│  │ with Context    │  │ FTS5/Date/ML    │  │ Mapping     │  │
│  └─────────────────┘  └─────────────────┘  └─────────────┘  │
├─────────────────────────────────────────────────────────────┤
│ Thread Pool Execution Layer                                 │
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────┐  │
│  │ Git Operations  │  │ Database Ops    │  │ Lazy ML     │  │
│  │ (2s timeout)    │  │ Single Conn     │  │ Loading     │  │
│  └─────────────────┘  └─────────────────┘  └─────────────┘  │
├─────────────────────────────────────────────────────────────┤
│ SQLite Database with FTS5 + Relationships                   │
│  ┌─────────────────────────────────────────────────────────┐│
│  │ entries + tags + relationships + embeddings + FTS       ││
│  └─────────────────────────────────────────────────────────┘│
└─────────────────────────────────────────────────────────────┘

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🔧 Technical Highlights

Performance & Security

• 10x faster startup - Lazy loading of ML dependencies (2-3s vs 14s)
• Thread-safe operations - Zero race conditions in tag creation
• WAL mode - Better concurrency and crash recovery
• Database lock prevention - Single-connection transactions
• Aggressive timeouts - Git operations fail-fast (2s per command)
• Input validation - Length limits, parameterized queries, SQL injection prevention

Semantic Search (Optional)

• Model: all-MiniLM-L6-v2 (384-dimensional embeddings)
• Storage: FAISS index for fast similarity search
• Graceful degradation: Works perfectly without ML dependencies

Data & Privacy

• Local-first: Single SQLite file, you own your data
• Portable: Move your .db file anywhere
• Secure: No external API calls, non-root Docker containers

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📚 Documentation

Full documentation available on the GitHub Wiki:

• Installation Guide
• Tools Reference
• Prompts Guide
• Relationship Visualization
• Examples & Tutorials
• Architecture Deep Dive

GitHub Gists: Practical Examples & Use Cases

→ View All Memory Journal Gists

Explore 5 curated gists with real-world examples and implementation patterns:

1. Complete Feature Showcase - All 15 tools, 8 prompts, and 3 resources
2. Relationship Mapping & Knowledge Graphs - Build knowledge graphs with typed relationships
3. Triple Search System Guide - Master FTS5, date range, and semantic search
4. Workflow Automation & Prompts - Standup, retrospectives, and weekly digests
5. Git Integration & Context Capture - Automatic project context from Git and GitHub

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🔗 Resources

• GitHub Wiki - Complete documentation
• GitHub Gists - 5 practical examples and use cases
• Docker Hub - Container images
• PyPI Package - Python package
• MCP Registry - Official MCP listing
• GitHub Issues - Bug reports & feature requests
• Adamic Support - Project announcements

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📄 License

MIT License - See LICENSE file for details.

🤝 Contributing

Built by developers, for developers. PRs welcome! See CONTRIBUTING.md for guidelines.