Memory MCP Server: Setup Guide, Use Cases & Limitations

What Is Memory MCP Server?

Memory MCP Server is a persistent key-value store that gives Claude the ability to retain and recall facts across separate conversations. It is maintained by the Model Context Protocol team as part of the official modelcontextprotocol/servers monorepo.

Quick Facts

What You Can Do With It

• Persistent memory: Store facts that survive session resets, so Claude does not start from scratch each conversation.
• User preferences: Save recurring preferences (tone, format, tooling choices) that Claude can retrieve and apply automatically.
• Cross-session context: Carry forward project state, decisions, or background information without re-pasting it each time.
• Knowledge graphs: Build structured networks of related entities and relationships that Claude can query and update incrementally.

How to Set It Up

Step 1 — Verify Node.js is installed (v18+ recommended):

node --version
**Step 2 — Test the server runs locally:**

```bash
npx @modelcontextprotocol/server-memory

Step 3 — Add it to your Claude config.

For Claude Desktop, edit claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["@modelcontextprotocol/server-memory"]
    }
  }
}

For Claude Code, add it to .claude/mcp.json in your project root, or use the CLI:

claude mcp add memory npx @modelcontextprotocol/server-memory

Restart the client after saving the config. Memory operations will then be available as tools Claude can call during any conversation.

Works Best With

Memory MCP Server is documented as compatible with Claude Desktop and Claude Code. Claude Desktop is the primary target for persistent cross-session memory — each conversation can read and write to the store without additional setup. Claude Code pairs well when you want project-scoped context (team conventions, architecture decisions, recurring commands) to persist across coding sessions.

Known Limitations

• Local storage only: Memory is stored on disk in the environment where the server runs. There is no built-in sync across machines or users.
• No authentication layer: The server does not enforce access controls. Any client connected to the MCP server can read and write all stored values — avoid storing sensitive credentials or private data.
• No automatic expiry: Keys persist indefinitely. There is no TTL mechanism; you must delete stale entries manually or via Claude tool calls.
• Size constraints: Very large memory stores may affect retrieval performance. The server is designed for structured facts and lightweight context, not bulk document storage.
• Cold-start writes: On first run, the store is empty. Claude will not have any prior memory until values have been explicitly written by a previous session.
• Model behavior: Whether Claude actually reads from or writes to memory depends on how it interprets the conversation. You may need to prompt it explicitly to use the memory tools.

Community Resources

• GitHub Issues — modelcontextprotocol/servers — bug reports and feature requests for the official server repo
• GitHub source for Memory MCP Server — implementation details and any recent changes
• r/ClaudeAI on Reddit — community threads on MCP setup and memory persistence patterns
• Anthropic Discord — #mcp channel for real-time discussion on MCP server configuration