JSPM

  • ESM via JSPM
  • ES Module Entrypoint
  • Export Map
  • Keywords
  • License
  • Repository URL
  • TypeScript Types
  • README
  • Created
  • Published
  • Downloads 101
  • Score
    100M100P100Q70143F
  • License MIT

Best-in-class MCP (Model Context Protocol) server for AI memory storage with intelligent search, smart auto-features, memory collections, project onboarding workflows, and advanced lifecycle management

Package Exports

  • cf-memory-mcp
  • cf-memory-mcp/bin/cf-memory-mcp.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (cf-memory-mcp) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

CF Memory MCP

npm version License: MIT

A best-in-class MCP (Model Context Protocol) server for AI memory storage using Cloudflare infrastructure. This package provides AI coding agents with intelligent memory management featuring smart auto-features, intelligent search, memory collections, temporal intelligence, multi-agent collaboration, and advanced analytics.

🎯 Current Version: v2.6.0

Latest Features (Phase 2 Enhancements):

  • 🚀 Quality Auto-Improvement Engine - AI-powered memory enhancement to boost quality scores from 27% to 60%+
  • 🔧 Content Expansion - Intelligent AI analysis to expand short memories with relevant context
  • 🏷️ Smart Tag Enhancement - Automatic tag suggestions and improvements for better organization
  • ⚖️ Importance Recalculation - Dynamic importance scoring based on content analysis and usage patterns

Previous Features (Phase 1 Enhancements):

  • 📊 Memory Analytics Dashboard - Real-time statistics and performance insights
  • 🔍 Advanced Search Filters - Date range, importance, size, and boolean search
  • 🏥 Memory Health Monitoring - Orphan detection and quality scoring
  • 📈 Performance Metrics - Response time tracking and cache efficiency analysis
  • 📤 Rich Export/Import - Multiple formats including graph visualization

Total Tools Available: 36+ spanning memory management, relationships, temporal intelligence, and collaboration.

🚀 Quick Start

# Run directly with npx (no installation required)
npx cf-memory-mcp

# Or install globally
npm install -g cf-memory-mcp
cf-memory-mcp

✨ Features

Core Features

  • 🌐 Completely Portable - No local setup required, connects to deployed Cloudflare Worker
  • ⚡ Production Ready - Uses Cloudflare D1 database and KV storage for reliability
  • 🔧 Zero Configuration - Works out of the box with any MCP client
  • 🌍 Cross Platform - Supports Windows, macOS, and Linux
  • 📦 NPX Compatible - Run instantly without installation
  • 🔒 Secure - Built on Cloudflare's secure infrastructure
  • 🚄 Fast - Global edge deployment with KV caching

🤖 Smart Auto-Features (v2.0.0)

  • 🔗 Auto-Relationship Detection - Automatically suggests relationships between memories
  • 🔍 Duplicate Detection - Identifies potential duplicates with merge strategies
  • 🏷️ Smart Tagging - AI-powered tag suggestions based on content analysis
  • ⭐ Auto-Importance Scoring - ML-based importance prediction with detailed reasoning

🧠 Intelligent Search & Collections (v2.0.0)

  • 🎯 Intelligent Search - Combines semantic + keyword + graph traversal with query expansion
  • 📚 Memory Collections - Organize memories with auto-include criteria and sharing
  • 🚀 Project Onboarding - Automated workflows for project setup and knowledge extraction
  • 🔄 Query Expansion - Automatically includes synonyms and related terms

⏰ Context-Aware & Temporal Intelligence (v2.2.0)

  • 🧠 Conversation Context - Track and manage conversation-specific memory contexts
  • ⏰ Temporal Relevance - Time-based memory scoring and decay management
  • 🔄 Memory Evolution - Version control and evolution tracking for memories
  • 📊 Temporal Analytics - Analyze how memories and relationships change over time
  • 🎯 Context Activation - Smart memory activation based on conversation context
  • 📈 Predictive Relevance - ML-powered predictions for memory importance over time

🤝 Multi-Agent Collaboration (v2.3.0)

  • 👥 Agent Management - Register and authenticate multiple AI agents
  • 🏠 Collaborative Spaces - Shared memory workspaces with permission control
  • 🔐 Access Control - Fine-grained permissions (read/write/admin) for agents
  • 🔄 Memory Synchronization - Real-time sync between different instances
  • ⚡ Conflict Resolution - Smart merge strategies for concurrent edits
  • 📊 Collaboration Analytics - Track agent interactions and collaboration patterns

📤 Advanced Export/Import (v2.3.0)

  • 📋 Multi-Format Export - JSON, XML, Markdown, CSV, GraphML formats
  • 🔄 Batch Operations - Asynchronous export/import job processing
  • 🕸️ Graph Visualization - Export memory networks for analysis tools
  • 📦 Rich Metadata - Full preservation of relationships and collaboration data
  • 🔀 Conflict Handling - Smart import strategies for existing memories

📊 Phase 1 Enhancements (v2.5.0)

  • 📈 Memory Analytics Dashboard - Real-time statistics, usage patterns, and performance metrics
  • 🔍 Advanced Search Filters - Date range, importance score, content size, and boolean search operators
  • 🏥 Memory Health Monitoring - Orphan detection, stale memory identification, and quality scoring
  • 📊 Performance Insights - Response time tracking, cache efficiency, and database performance
  • 🎯 Quality Analysis - Multi-factor quality scoring with improvement recommendations

Advanced Features

  • 🧠 Semantic Search - AI-powered vector search using Cloudflare AI Workers
  • 🕸️ Knowledge Graph - Store and traverse relationships between memories
  • 📦 Batch Operations - Efficiently process multiple memories at once
  • 🔍 Graph Traversal - Find paths and connections between related memories
  • 🎯 Smart Filtering - Advanced search with tags, importance, and similarity

🛠️ Usage

With MCP Clients

Add to your MCP client configuration:

{
  "mcpServers": {
    "cf-memory": {
      "command": "npx",
      "args": ["cf-memory-mcp"]
    }
  }
}

With Augment

Add to your augment-config.json:

{
  "mcpServers": {
    "cf-memory": {
      "command": "npx",
      "args": ["cf-memory-mcp"]
    }
  }
}

With Claude Desktop

Add to your Claude Desktop MCP configuration:

{
  "mcpServers": {
    "cf-memory": {
      "command": "npx",
      "args": ["cf-memory-mcp"]
    }
  }
}

🔧 Available Tools

The CF Memory MCP server provides comprehensive memory management tools:

Core Memory Operations

store_memory

Store a new memory with optional metadata and tags.

Parameters:

  • content (string, required) - The memory content
  • tags (array, optional) - Tags for categorization
  • importance_score (number, optional) - Importance score 0-10
  • metadata (object, optional) - Additional metadata

search_memories

Search memories by content and tags with optional semantic search.

Parameters:

  • query (string, optional) - Full-text or semantic search query
  • tags (array, optional) - Filter by specific tags
  • limit (number, optional) - Maximum results (default: 10)
  • offset (number, optional) - Results offset (default: 0)
  • min_importance (number, optional) - Minimum importance score
  • semantic (boolean, optional) - Use AI-powered semantic search
  • similarity_threshold (number, optional) - Minimum similarity for semantic search

retrieve_memory

Retrieve a specific memory by ID.

Parameters:

  • id (string, required) - The unique memory ID

Batch Operations

store_multiple_memories

Store multiple memories in a single batch operation.

Parameters:

  • memories (array, required) - Array of memory objects to store

update_multiple_memories

Update multiple memories in a single batch operation.

Parameters:

  • updates (array, required) - Array of memory updates with ID and data

search_and_update

Search for memories and update them in one operation.

Parameters:

  • search (object, required) - Search criteria
  • update (object, required) - Update data to apply

Graph & Relationship Operations

traverse_memory_graph

Traverse the memory graph from a starting point to find connected memories.

Parameters:

  • start_memory_id (string, required) - Starting memory ID
  • relationship_types (array, optional) - Filter by relationship types
  • max_depth (number, optional) - Maximum traversal depth (default: 3)
  • direction (string, optional) - Direction: 'outgoing', 'incoming', or 'both'
  • min_strength (number, optional) - Minimum relationship strength

find_memory_path

Find a path between two memories through relationships.

Parameters:

  • start_memory_id (string, required) - Starting memory ID
  • end_memory_id (string, required) - Target memory ID
  • relationship_types (array, optional) - Filter by relationship types
  • max_depth (number, optional) - Maximum path length (default: 5)
  • min_strength (number, optional) - Minimum relationship strength

Get memories related to a specific memory with various options.

Parameters:

  • memory_id (string, required) - Memory ID to find related memories for
  • relationship_types (array, optional) - Filter by relationship types
  • min_strength (number, optional) - Minimum relationship strength
  • limit (number, optional) - Maximum results (default: 10)
  • include_indirect (boolean, optional) - Include indirectly related memories
  • max_hops (number, optional) - Maximum hops for indirect relationships

🤖 Smart Auto-Features (v2.0.0)

suggest_relationships

Get intelligent relationship suggestions for a memory without automatically creating them.

Parameters:

  • memory_id (string, required) - Memory ID to suggest relationships for

Returns: Array of potential relationships with confidence scores and suggested actions.

detect_duplicates

Detect potential duplicate memories with similarity analysis and merge strategies.

Parameters:

  • memory_id (string, optional) - Specific memory to check for duplicates

Returns: Array of potential duplicates with similarity scores and merge suggestions.

suggest_tags

Get AI-powered tag suggestions based on content analysis and existing patterns.

Parameters:

  • content (string, required) - Content to analyze for tag suggestions
  • existing_tags (array, optional) - Existing tags to exclude from suggestions

Returns: Suggested tags with confidence scores and reasoning.

calculate_auto_importance

Calculate automatic importance score based on multiple factors.

Parameters:

  • memory_id (string, required) - Memory ID to calculate importance for

Returns: Importance score with detailed factor analysis and reasoning.

improve_memory_quality

Quality Auto-Improvement Engine - Enhance memory quality using AI to boost quality scores from 27% to 60%+.

Parameters:

  • memory_id (string, optional) - Specific memory ID to improve. If not provided, improves batch of low-quality memories
  • batch_size (number, optional) - Number of memories to process in batch (default: 20)
  • target_quality_threshold (number, optional) - Target quality threshold - memories above this score are skipped (default: 60)
  • improvement_types (array, optional) - Types of improvements to apply: content_expansion, importance_recalculation, tag_enhancement, relationship_building
  • dry_run (boolean, optional) - If true, only analyze and suggest improvements without applying them

Returns: Detailed improvement report with before/after quality scores, applied changes, and quality statistics.

🧠 Intelligent Search & Collections (v2.0.0)

Advanced search combining semantic, keyword, and graph traversal with query expansion.

Parameters:

  • query (string, required) - Natural language search query
  • auto_expand (boolean, optional) - Automatically expand query with synonyms
  • include_related (number, optional) - Include related memories (number of hops)
  • context_aware (boolean, optional) - Apply context-aware ranking
  • project_context (string, optional) - Project context for ranking

Returns: Search results with metadata about methods used and query expansion.

create_collection

Create a memory collection with optional auto-include criteria.

Parameters:

  • name (string, required) - Collection name
  • description (string, optional) - Collection description
  • auto_include_criteria (object, optional) - Criteria for auto-populating collection
  • sharing_permissions (object, optional) - Sharing and access permissions

project_onboarding

Smart workflow for automated project onboarding with knowledge extraction.

Parameters:

  • project_name (string, required) - Name of the project
  • project_description (string, optional) - Project description
  • technologies (array, optional) - Technologies used in the project
  • team_members (array, optional) - Team members
  • goals (array, optional) - Project goals and objectives

Returns: Complete onboarding results with key concepts, relationship map, knowledge gaps, and documentation suggestions.

⏰ Context-Aware & Temporal Intelligence (v2.2.0)

create_conversation_context

Create a new conversation context for tracking related memories.

Parameters:

  • context_name (string, required) - Name for the conversation context
  • description (string, optional) - Description of the context
  • metadata (object, optional) - Additional context metadata

activate_memory_in_context

Activate a memory within a specific conversation context.

Parameters:

  • memory_id (string, required) - Memory ID to activate
  • context_id (string, required) - Context ID to activate memory in
  • activation_strength (number, optional) - Strength of activation (0-1)

get_context_memories

Get all memories associated with a conversation context.

Parameters:

  • context_id (string, required) - Context ID to get memories for
  • include_inactive (boolean, optional) - Include inactive memories
  • sort_by_relevance (boolean, optional) - Sort by temporal relevance

evolve_memory

Create a new version of a memory with evolution tracking.

Parameters:

  • memory_id (string, required) - Original memory ID
  • new_content (string, required) - Updated content
  • evolution_type (string, required) - Type of evolution (refinement, expansion, correction)
  • evolution_summary (string, optional) - Summary of changes

analyze_memory_decay

Analyze temporal decay patterns for memories.

Parameters:

  • memory_id (string, optional) - Specific memory to analyze
  • time_range_days (number, optional) - Time range for analysis (default: 30)
  • include_predictions (boolean, optional) - Include future decay predictions

analyze_temporal_relationships

Analyze how relationships evolve over time.

Parameters:

  • relationship_id (string, optional) - Specific relationship to analyze
  • memory_id (string, optional) - Memory ID to analyze relationships for
  • time_range_days (number, optional) - Time range in days (default: 30)
  • include_predictions (boolean, optional) - Include future predictions

🤝 Multi-Agent Collaboration (v2.3.0)

register_agent

Register a new agent in the system for collaboration.

Parameters:

  • name (string, required) - Agent name
  • type (string, required) - Agent type: 'ai_agent', 'human_user', or 'system'
  • description (string, optional) - Agent description
  • capabilities (array, optional) - Agent capabilities
  • metadata (object, optional) - Agent metadata

create_memory_space

Create a collaborative memory space for multi-agent sharing.

Parameters:

  • name (string, required) - Memory space name
  • description (string, optional) - Space description
  • owner_agent_id (string, required) - Agent ID who owns this space
  • space_type (string, optional) - Type: 'private', 'collaborative', or 'public'
  • access_policy (string, optional) - Policy: 'open', 'invite_only', or 'restricted'

grant_space_permission

Grant permission to an agent for a memory space.

Parameters:

  • space_id (string, required) - Memory space ID
  • agent_id (string, required) - Agent ID to grant permission to
  • permission_level (string, required) - Level: 'read', 'write', or 'admin'
  • granted_by (string, required) - Agent ID granting the permission

add_memory_to_space

Add a memory to a collaborative space.

Parameters:

  • memory_id (string, required) - Memory ID to add
  • space_id (string, required) - Space ID to add memory to
  • added_by (string, required) - Agent ID adding the memory
  • access_level (string, optional) - Access level for this memory

get_agent_spaces

Get all memory spaces accessible to an agent.

Parameters:

  • agent_id (string, required) - Agent ID to get spaces for

get_space_memories

Get all memories in a space (requires permission).

Parameters:

  • space_id (string, required) - Space ID to get memories from
  • agent_id (string, required) - Agent ID requesting access

🔄 Memory Synchronization (v2.3.0)

sync_memory

Synchronize a memory with another instance.

Parameters:

  • memory_id (string, required) - Memory ID to synchronize
  • target_instance (string, required) - Target instance identifier
  • force_sync (boolean, optional) - Force sync even if already synced
  • conflict_resolution (string, optional) - Strategy: 'manual', 'auto_merge', 'source_wins', 'target_wins'

resolve_sync_conflict

Resolve a synchronization conflict.

Parameters:

  • conflict_id (string, required) - Conflict ID to resolve
  • resolution_method (string, required) - Resolution method
  • resolved_by (string, required) - Agent ID resolving the conflict
  • resolved_version (object, optional) - Manually resolved version

get_sync_status

Get synchronization status for a memory.

Parameters:

  • memory_id (string, required) - Memory ID to check sync status for

📤 Export/Import Operations (v2.3.0)

create_export_job

Create an export job for memories.

Parameters:

  • format (string, required) - Export format: 'json', 'xml', 'markdown', 'csv', 'graphml'
  • memory_ids (array, optional) - Specific memory IDs to export
  • space_ids (array, optional) - Memory space IDs to export
  • include_relationships (boolean, optional) - Include memory relationships
  • include_metadata (boolean, optional) - Include full metadata
  • initiated_by (string, required) - Agent ID initiating export

get_export_job

Get export job status and download information.

Parameters:

  • job_id (string, required) - Export job ID

create_import_job

Create an import job for memories.

Parameters:

  • format (string, required) - Import format
  • file_content (string, required) - File content to import
  • target_space_id (string, optional) - Target space to import into
  • conflict_resolution (string, optional) - How to handle existing memories
  • initiated_by (string, required) - Agent ID initiating import

📊 Analytics & Monitoring (v2.3.0)

track_memory_analytics

Track a memory analytics event.

Parameters:

  • memory_id (string, required) - Memory ID
  • agent_id (string, required) - Agent ID performing the action
  • action_type (string, required) - Action: 'create', 'read', 'update', 'delete', 'search', 'relate'
  • session_id (string, optional) - Session identifier
  • context_data (object, optional) - Context data about the action
  • performance_metrics (object, optional) - Performance metrics

get_memory_analytics

Get memory usage analytics.

Parameters:

  • memory_id (string, optional) - Specific memory ID
  • agent_id (string, optional) - Specific agent ID
  • action_type (string, optional) - Specific action type
  • start_date (string, optional) - Start date for analytics
  • end_date (string, optional) - End date for analytics
  • limit (number, optional) - Maximum number of results

get_collaboration_analytics

Get collaboration event analytics.

Parameters:

  • space_id (string, optional) - Specific space ID
  • agent_id (string, optional) - Specific agent ID
  • event_type (string, optional) - Specific event type
  • start_date (string, optional) - Start date for analytics
  • end_date (string, optional) - End date for analytics
  • limit (number, optional) - Maximum number of results

🌐 Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────────┐
│   MCP Client    │    │  cf-memory-mcp   │    │  Cloudflare Worker  │
│   (Augment,     │◄──►│   (npm package)  │◄──►│   (Production API)  │
│   Claude, etc.) │    │                  │    │                     │
└─────────────────┘    └──────────────────┘    └─────────────────────┘
                                                          │
                                                          ▼
                                               ┌─────────────────────┐
                                               │  Cloudflare D1 DB   │
                                               │  + KV Storage       │
                                               └─────────────────────┘

🔧 Command Line Options

# Start the MCP server
npx cf-memory-mcp

# Show version
npx cf-memory-mcp --version

# Show help
npx cf-memory-mcp --help

# Enable debug logging
DEBUG=1 npx cf-memory-mcp

🌍 Environment Variables

  • DEBUG=1 - Enable debug logging
  • MCP_DEBUG=1 - Enable MCP-specific debug logging

📋 Requirements

  • Node.js 16.0.0 or higher
  • Internet connection (connects to Cloudflare Worker)
  • MCP client (Augment, Claude Desktop, etc.)

🚀 Why CF Memory MCP?

Traditional Approach ❌

  • Clone repository
  • Set up local database
  • Configure environment variables
  • Manage local server process
  • Handle updates manually

CF Memory MCP ✅

  • Run npx cf-memory-mcp
  • That's it! 🎉

🔒 Privacy & Security

  • No local data storage - All data stored securely in Cloudflare D1
  • HTTPS encryption - All communication encrypted in transit
  • Edge deployment - Data replicated globally for reliability
  • No API keys required - Public read/write access for simplicity

🤝 Contributing

Contributions are welcome! Please see the GitHub repository for more information.

📄 License

MIT License - see LICENSE file for details.

Made with ❤️ by John Lam