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MCP Memory Server Implementation Plan

Universal AI Memory System for Collaborative Development

A comprehensive plan for implementing a Supermemory-like MCP (Model Context Protocol) memory server that works across different AI coding tools (Windsurf, Cline, Roo Code, KiloCode, Claude Desktop, etc.).

🎯 Project Goals

  • Universal Compatibility: Work with any MCP-compatible AI tool
  • Persistent Context: Maintain project knowledge across sessions
  • Semantic Search: Natural language queries for retrieving context
  • Lightweight: Easy to set up and maintain
  • Privacy-First: Local-first storage with optional cloud sync

📋 Implementation Phases

Phase 1: Core Memory Server (MVP)

Goal: Basic MCP server with CRUD operations for memories

1.1 Server Setup

  • Initialize Node.js/TypeScript MCP server project
  • Implement MCP protocol handlers (stdio transport)
  • Set up SQLite database for local storage
  • Define memory schema (id, content, tags, timestamp, metadata)
  • Add basic CRUD operations (create, read, update, delete)

1.2 Core Tools

  • addMemory - Store new memory with tags and metadata
  • searchMemories - Search by keywords or tags
  • getMemory - Retrieve specific memory by ID
  • listMemories - List recent memories with pagination
  • deleteMemory - Remove memory by ID

1.3 Configuration

  • Create configuration file (.mcp-memory/config.json)
  • Support environment variables for settings
  • Add database path configuration
  • Implement logging system

1.4 Testing & Documentation

  • Unit tests for core operations
  • Integration tests with MCP protocol
  • Quick start guide
  • API documentation

Deliverable: Working MCP server with basic memory operations

Phase 2: Semantic Search & Vector Storage

Goal: Add semantic search capabilities using embeddings

2.1 Vector Database Integration

  • Evaluate vector storage options:
    • SQLite-vec (lightweight, local)
    • Chroma (feature-rich, local)
    • Qdrant (scalable, can be local or cloud)
  • Implement chosen vector database
  • Set up embedding pipeline (OpenAI, local models, etc.)
  • Add vector similarity search

2.2 Enhanced Search Tools

  • semanticSearch - Natural language search using embeddings
  • similarMemories - Find related memories
  • searchByTimeRange - Temporal queries
  • searchByTags - Tag-based filtering
  • Hybrid search (keyword + semantic)

2.3 Memory Types & Schema

  • Define memory types:
    • note - General notes and observations
    • decision - Architecture and design decisions
    • task - TODO items and action items
    • code_ref - Code snippets and references
    • context - Project context and background
  • Add metadata fields (author, project, importance)
  • Implement memory relationships/links

2.4 Performance & Quality

  • Benchmark retrieval quality
  • Optimize query performance
  • Add caching layer
  • Implement deduplication

Deliverable: Semantic search with vector embeddings

Phase 3: Auto-Indexing & Context Awareness

Goal: Automatically capture and index project knowledge

3.1 Auto-Indexing Pipeline

  • Watch file system for changes
  • Index code files and documentation
  • Extract and index git commits
  • Index AI chat sessions (if available)
  • Chunking strategy for large files

3.2 Context Extraction

  • Extract code structure (functions, classes, modules)
  • Parse documentation (README, guides, comments)
  • Extract decisions from commit messages
  • Identify patterns and conventions

3.3 Smart Indexing

  • Incremental indexing (only changed files)
  • Importance scoring for memories
  • Automatic tag generation
  • Duplicate detection and merging

3.4 Management Tools

  • reindexProject - Full project re-indexing
  • indexStatus - Show indexing health
  • pruneMemories - Remove outdated/duplicate memories
  • exportMemories - Export to JSON/Markdown
  • importMemories - Import from external sources

Deliverable: Automatic project knowledge capture

Phase 4: Advanced Features

Goal: Enhanced capabilities for team collaboration

4.1 Temporal Knowledge Graph

  • Model entities and relationships
  • Track knowledge evolution over time
  • "Who/what/when/why" queries
  • Visualize knowledge connections

4.2 Team Collaboration

  • Multi-user support
  • Shared memory spaces
  • Memory permissions and visibility
  • Conflict resolution for concurrent edits

4.3 Integration Features

  • GitHub integration (issues, PRs, discussions)
  • Slack/Discord integration for team memories
  • Jira/Linear integration for task tracking
  • Export to Notion/Obsidian

4.4 Memory Management

  • Retention policies (auto-archive old memories)
  • Memory importance scoring
  • Automatic summarization of old memories
  • Memory analytics and insights

Deliverable: Production-ready collaborative memory system

🛠️ Technical Stack

Core Technologies

  • Language: TypeScript/Node.js
  • Protocol: MCP (Model Context Protocol)
  • Database: SQLite (primary) + Vector DB (Chroma/SQLite-vec)
  • Embeddings: OpenAI API or local models (sentence-transformers)
  • Transport: stdio (standard MCP transport)

Dependencies

{
  "@modelcontextprotocol/sdk": "^0.5.0",
  "better-sqlite3": "^9.0.0",
  "chromadb": "^1.8.0",
  "openai": "^4.0.0",
  "zod": "^3.22.0"
}

🔧 Configuration Example

{
  "storage": {
    "type": "sqlite",
    "path": ".mcp-memory/memories.db"
  },
  "vectorStore": {
    "enabled": true,
    "provider": "chroma",
    "path": ".mcp-memory/vectors"
  },
  "embeddings": {
    "provider": "openai",
    "model": "text-embedding-3-small",
    "apiKey": "${OPENAI_API_KEY}"
  },
  "autoIndex": {
    "enabled": true,
    "watchPaths": ["src/", "docs/"],
    "ignorePaths": ["node_modules/", "dist/"]
  },
  "retention": {
    "maxAge": "90d",
    "maxMemories": 10000
  }
}

🎨 AI Tool Integration

Windsurf

// .windsurf/mcp.json
{
  "mcpServers": {
    "memory": {
      "command": "node",
      "args": ["/path/to/mcp-memory-server/dist/index.js"],
      "env": {
        "OPENAI_API_KEY": "${OPENAI_API_KEY}"
      }
    }
  }
}

Cline / Roo Code

// MCP settings
{
  "mcpServers": {
    "memory": {
      "command": "node",
      "args": ["/path/to/mcp-memory-server/dist/index.js"]
    }
  }
}

Claude Desktop

// ~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "memory": {
      "command": "node",
      "args": ["/path/to/mcp-memory-server/dist/index.js"]
    }
  }
}

📊 Success Metrics

  • Retrieval Quality: >80% relevant results in top 5
  • Performance: <100ms for keyword search, <500ms for semantic search
  • Storage Efficiency: <10MB per 1000 memories
  • Uptime: 99.9% availability
  • User Satisfaction: Positive feedback from team

🚀 Getting Started (Post-Implementation)

# Install the MCP memory server
npm install -g @vibecode/mcp-memory-server

# Initialize in your project
mcp-memory init

# Configure your AI tool
mcp-memory configure --tool windsurf

# Start using it
# AI tools will automatically have access to memory operations

📚 Resources

🤝 Contributing

This is a community-driven project. Contributions welcome!

  1. Start with Phase 1 (MVP)
  2. Follow the implementation plan
  3. Add tests for all features
  4. Update documentation
  5. Submit PR with clear description

Status: Planning Phase
Target Start: Q1 2025
Maintainer: Vibecode Community