mcp-assoc-memory 0.1.1

Associative Memory MCP Server for LLMs - Knowledge management system with hierarchical scope organization

MCP Associative Memory Server

🧠 Production-Ready Intelligent Memory System - Store, search, and discover knowledge connections using the Model Context Protocol (MCP) with 74/74 tests passing and complete CI/CD pipeline.

🏆 Production Status (July 2025)

✅ ENTERPRISE-READY:

• 74/74 tests passing (100% success rate)
• Complete CI/CD pipeline with security and quality gates
• 10 MCP tools for comprehensive memory management
• Sub-second performance with optimized vector search
• Docker containerized for production deployment

🌟 Overview

Transform your development workflow with an AI-powered memory system that:

• Stores insights from your daily work and learning
• Finds related knowledge when you need it most
• Discovers unexpected connections between ideas
• Organizes knowledge in intuitive hierarchical scopes
• Syncs across environments for seamless workflow integration

Built with FastMCP 2.0 for modern LLM integration, optimized for GitHub Copilot workflows.

✨ Key Features

🧠 Intelligent Memory Operations

• Semantic Search: Find relevant memories using natural language queries
• Association Discovery: Automatically discover connections between concepts
• Complete CRUD: Create, Read, Update, Delete with full lifecycle management
• Smart Organization: Hierarchical scopes with auto-categorization

🔍 Advanced Discovery

• Top-K Search: Optimized threshold (0.1) with LLM-guided relevance judgment
• Cross-Scope Associations: Find connections across different knowledge scopes
• Similarity Scoring: Transparent relevance metrics for intelligent filtering
• Creative Connections: Discover unexpected relationships for innovation

🗂️ Powerful Organization

• Hierarchical Scopes: work/projects/name, learning/technology, personal/ideas
• Flexible Categorization: Tags, metadata, and automatic scope suggestions
• Session Management: Temporary workspaces for project isolation
• Memory Movement: Reorganize knowledge as understanding evolves

🔄 Cross-Environment Sync

• Export/Import: Backup and restore memories across development environments
• Multiple Formats: JSON, YAML with compression support
• Merge Strategies: Handle duplicates intelligently during sync
• Git Workflow: Integrate memory backup into version control processes

🛠️ Developer Experience

• GitHub Copilot Integration: Natural language memory operations
• VS Code Tasks: One-click server management and maintenance
• Real-time Association: Automatic relationship discovery during storage
• Performance Optimized: Sub-second search across thousands of memories
• Response Level Control: Minimal, standard, or full detail responses for optimal token usage

⚡ Smart Response Levels

Control response detail and token usage with three intelligent levels:

• minimal: Essential information only (~50 tokens) - Perfect for status checks and basic operations
• standard: Balanced detail for workflow continuity (default) - Optimal for most use cases
• full: Comprehensive data including metadata, associations, and analysis - Ideal for debugging and detailed exploration

Example Usage:

# Quick status check
memory_store(content="meeting notes", response_level="minimal")
# Returns: {"success": true, "message": "Memory stored", "memory_id": "..."}

# Full debugging info
memory_search(query="project ideas", response_level="full") 
# Returns: Complete results with similarity scores, metadata, associations

🎯 Complete MCP Tool Suite

🚀 Modern API (10 Clean Tools)

Core Operations (Primary API)

• memory_store - Store new memories with auto-association
• memory_search - Unified search with standard and diversified modes
• memory_manage - Get, update, and delete memory operations
• memory_sync - Import and export memories for backup/sync

Discovery and Analysis

• memory_discover_associations - Find semantically related memories
• memory_list_all - Browse complete memory collection with pagination

Organization Management

• scope_list - Browse hierarchical memory organization
• scope_suggest - AI-powered scope recommendations
• memory_move - Reorganize memories into better categories

Session Management

• session_manage - Create, list, and cleanup temporary working sessions

🎯 Clean, Modern API

All tools use intuitive, natural names with powerful unified interfaces for better developer experience.

📚 Comprehensive Documentation

🚀 Quick Start Guide

Get up and running in 5 minutes with essential commands and patterns.

💡 Best Practices

Comprehensive guide to optimizing your associative memory workflow.

🔧 API Reference

Complete technical documentation for all MCP tools and parameters.

🏢 Real-World Examples

Practical usage patterns for developers, teams, and organizations.

🆘 Troubleshooting Guide

Solutions for common issues and system maintenance procedures.

📊 Sample Data

Ready-to-import memory dataset with 28 curated memories demonstrating system capabilities.

🚀 Complete Documentation →

Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   LLM Client    │────│  FastMCP Server │────│  Memory Store   │
│                 │    │                 │    │                 │
│ - Claude        │    │ - @app.tool()   │    │ - ChromaDB      │
│ - ChatGPT       │    │ - @app.resource()│   │ - SQLite        │
│ - Custom LLM    │    │ - @app.prompt() │    │ - NetworkX      │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Technology Stack

• Language: Python 3.11+
• MCP Framework: FastMCP 2.0
• Vector Database: ChromaDB
• Embedding Model: OpenAI Embeddings / Sentence Transformers
• Graph Database: NetworkX (in-memory)
• Storage: SQLite (metadata)

Installation & Usage

For detailed setup instructions, see docs/installation.md.

Server Startup

Recommended: FastMCP Server (STDIO default)

Official startup method:

python -m mcp_assoc_memory

The server always starts in STDIO mode by default for MCP client integration. The legacy mcp-server command is deprecated and no longer provided.

Environment Variables

• OPENAI_API_KEY: Required for OpenAI embeddings
• MCP_LOG_LEVEL: Set logging level (DEBUG, INFO, WARNING, ERROR)

Server Control Scripts

• Start: ./scripts/mcp_server_daemon.sh start
• Stop: ./scripts/mcp_server_daemon.sh stop
• Restart: ./scripts/mcp_server_daemon.sh restart
• Status: ./scripts/mcp_server_daemon.sh status

Logs & PID Files

• Logs: logs/mcp_server.log
• PID: logs/mcp_server.pid

🛠️ Installation (PyPI, pipx, GitHub)

Recommended: PyPI (after publish)

pip install mcp-assoc-memory

pipx (isolated global install)

pipx install mcp-assoc-memory

GitHub (latest/dev version)

pip install git+https://github.com/mako10k/mcp-assoc-memory.git
# or
pipx install git+https://github.com/mako10k/mcp-assoc-memory.git

Start the server (after install)

python -m mcp_assoc_memory

• MCPクライアントや自動検出ツール（Claude Desktop Extensions, FastMCP, Cursor等）からも自動認識されます。
• Dockerイメージも近日公開予定。

---

Developer Information

Development Guidelines

🤖 AI Development Agent: development/workflow/AGENT.md
📋 GitHub Copilot Rules: .github/copilot-instructions.md
🔄 Development Workflow: development/workflow/DEVELOPER_GUIDELINES.md

---

✅ Quality Status

All code passes mypy (type check), flake8 (lint), and pytest (unit/integration tests) as of July 2025.
CI/CD pipeline enforces these checks for every commit.

Technical Reference

• System Architecture - Architecture and structure documentation
• Technical Specifications - API specs and feature details
• Security & Configuration - Authentication and transport configuration
• Knowledge Base - Curated development knowledge
• Complete Development Docs →

Contributing

1. Check development guidelines before contributing
2. Review architecture documentation for system understanding
3. Follow GitHub Copilot instructions for AI-assisted development
4. Update relevant documentation when making changes

🚀 Quick Start

1. Clone the repository

git clone https://github.com/mako10k/mcp-assoc-memory.git
cd mcp-assoc-memory

2. Set up your environment

python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate  # Windows

3. Install dependencies

pip install -r requirements.txt
pip install -r requirements-dev.txt

4. Run tests and linting

python scripts/smart_lint.py
pytest tests/ -v

5. Start the MCP server

./scripts/mcp_server_daemon.sh start

For Docker users:

docker-compose up --build

---

License

MIT License