Which integrations are available for this server?

Provides persistent storage of conversation memories using MongoDB database with support for saving, retrieving, adding, and clearing memory records.

How do I use Memory MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Memory MCP save that I'm working on a React project with TypeScript" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

🧠 Memory MCP Server

Persistent memory and context window caching for LLM conversations. Save, retrieve, and manage memories with intelligent context archiving. MongoDB-backed storage.

An MCP (Model Context Protocol) server that provides memory management and context window caching for AI coding environments like Cursor and Claude Desktop.

Trust Score

Why Use Memory MCP?

💾 Persistent Storage – MongoDB-backed memory that survives sessions
🧠 Context Caching – Intelligent archiving and retrieval of conversation context
🏷️ Tag-based Search – Organize and find memories by tags
📊 Relevance Scoring – Automatically score archived content relevance
⚡ Easy Setup – One-click install in Cursor or simple manual setup

Related MCP server: MongoDB MCP Server for LLMs

Quick Start

Ready to add memory to your AI workflow? Install in seconds:

Install in Cursor (Recommended):

🔗 Install in Cursor

Or install manually:

npm install -g @jamesanz/memory-mcp # Or from source: git clone https://github.com/JamesANZ/memory-mcp.git cd memory-mcp && npm install && npm run build

Features

Basic Memory Tools

save-memories – Save memories to database (overwrites existing)
get-memories – Retrieve all stored memories
add-memories – Append new memories without overwriting
clear-memories – Remove all stored memories

Context Window Caching

archive-context – Archive conversation context with tags
retrieve-context – Retrieve relevant archived context
score-relevance – Score archived content relevance
create-summary – Create summaries of archived content
get-conversation-summaries – Get all summaries for a conversation
search-context-by-tags – Search archived content by tags

Installation

Cursor (One-Click)

Click the install link above or use:

cursor://anysphere.cursor-deeplink/mcp/install?name=memory-mcp&config=eyJtZW1vcnktbWNwIjp7ImNvbW1hbmQiOiJucHgiLCJhcmdzIjpbIi15IiwiQGphbWVzYW56L21lbW9yeS1tY3AiXX19

Manual Installation

Requirements: Node.js 18+, npm, MongoDB

# Clone and build git clone https://github.com/JamesANZ/memory-mcp.git cd memory-mcp npm install npm run build # Set MongoDB connection string export MONGODB_URI="mongodb://localhost:27017" # Run server npm start

Claude Desktop

Add to claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

{ "mcpServers": { "memory-mcp": { "command": "node", "args": ["/absolute/path/to/memory-mcp/build/index.js"], "env": { "MONGODB_URI": "mongodb://localhost:27017" } } } }

Restart Claude Desktop after configuration.

Configuration

Set the MongoDB connection string:

export MONGODB_URI="mongodb://localhost:27017"

Default: mongodb://localhost:27017

Usage Examples

Save Memories

Store memories from a conversation:

{ "tool": "save-memories", "arguments": { "memories": ["User prefers TypeScript", "User works on blockchain projects"], "llm": "claude", "userId": "user123" } }

Retrieve Memories

Get all stored memories:

{ "tool": "get-memories", "arguments": {} }

Archive Context

Archive conversation context when it gets too long:

{ "tool": "archive-context", "arguments": { "conversationId": "conv-123", "contextMessages": ["Message 1", "Message 2"], "tags": ["coding", "typescript"], "llm": "claude" } }

Retrieve Relevant Context

Get archived content relevant to current conversation:

{ "tool": "retrieve-context", "arguments": { "conversationId": "conv-123", "tags": ["coding"], "minRelevanceScore": 0.5, "limit": 10 } }

Context Window Caching

The system automatically:

Archives content when context usage reaches 80%
Retrieves relevant content when usage drops below 30%
Scores relevance using keyword overlap
Creates summaries to condense long conversations

Use Cases

Long Conversations – Manage context windows for extended sessions
Memory Persistence – Save important information across sessions
Context Retrieval – Bring back relevant past conversations
Research Projects – Organize and tag research conversations

Technical Details

Built with: Node.js, TypeScript, MCP SDK, MongoDB
Dependencies: @modelcontextprotocol/sdk, mongodb, zod
Platforms: macOS, Windows, Linux

Storage: MongoDB (default: mongodb://localhost:27017)

Contributing

⭐ If this project helps you, please star it on GitHub! ⭐

Contributions welcome! Please open an issue or submit a pull request.

License

ISC

Support

If you find this project useful, consider supporting it:

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