MCP 服务器

Mnemoverse Memory

Persistent long-term memory for AI agents — semantic recall across Claude, Cursor, ChatGPT & MCP.

README

@mnemoverse/mcp-memory-server

Shared AI memory across Claude, Cursor, VS Code, ChatGPT, and any MCP client. Write once, recall anywhere.

Your agent remembers everything — across sessions, projects, and tools. One API key, one memory, everywhere.

Quick Start

1. Get a free API key

2. Connect to your AI tool

Claude Code — add via CLI:

claude mcp add mnemoverse \
  -e MNEMOVERSE_API_KEY=mk_live_YOUR_KEY \
  -- npx -y @mnemoverse/mcp-memory-server@latest

Cursor — click to install, or add to .cursor/mcp.json:

{
  "mcpServers": {
    "mnemoverse": {
      "command": "npx",
      "args": [
        "-y",
        "@mnemoverse/mcp-memory-server@latest"
      ],
      "env": {
        "MNEMOVERSE_API_KEY": "mk_live_YOUR_KEY"
      }
    }
  }
}

VS Code — add to .vscode/mcp.json (note: VS Code uses servers, not mcpServers):

{
  "servers": {
    "mnemoverse": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@mnemoverse/mcp-memory-server@latest"
      ],
      "env": {
        "MNEMOVERSE_API_KEY": "mk_live_YOUR_KEY"
      }
    }
  }
}

Windsurf — add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "mnemoverse": {
      "command": "npx",
      "args": [
        "-y",
        "@mnemoverse/mcp-memory-server@latest"
      ],
      "env": {
        "MNEMOVERSE_API_KEY": "mk_live_YOUR_KEY"
      }
    }
  }
}

More MCP clients — same server, different config file:

Zed — add to ~/.config/zed/settings.json (Zed uses context_servers, and "source": "custom" is required):

{
  "context_servers": {
    "mnemoverse": {
      "source": "custom",
      "command": "npx",
      "args": [
        "-y",
        "@mnemoverse/mcp-memory-server@latest"
      ],
      "env": {
        "MNEMOVERSE_API_KEY": "mk_live_YOUR_KEY"
      }
    }
  }
}

JetBrains (AI Assistant) — Settings → Tools → AI Assistant → Model Context Protocol (MCP), then paste:

{
  "mcpServers": {
    "mnemoverse": {
      "command": "npx",
      "args": [
        "-y",
        "@mnemoverse/mcp-memory-server@latest"
      ],
      "env": {
        "MNEMOVERSE_API_KEY": "mk_live_YOUR_KEY"
      }
    }
  }
}

Cline — MCP Servers → Configure (or edit cline_mcp_settings.json). Cline reads env values literally, so paste your real key — not a ${VAR} reference:

{
  "mcpServers": {
    "mnemoverse": {
      "command": "npx",
      "args": [
        "-y",
        "@mnemoverse/mcp-memory-server@latest"
      ],
      "env": {
        "MNEMOVERSE_API_KEY": "mk_live_YOUR_KEY"
      }
    }
  }
}

Continue — add ~/.continue/mcpServers/mnemoverse.yaml (Continue uses YAML):

mcpServers:
  - name: mnemoverse
    command: npx
    args:
      - "-y"
      - "@mnemoverse/mcp-memory-server@latest"
    env:
      MNEMOVERSE_API_KEY: "mk_live_YOUR_KEY"

Why @latest? Bare npx @mnemoverse/mcp-memory-server is cached indefinitely by npm and stops re-checking the registry. The @latest suffix forces a metadata lookup on every Claude Code / Cursor / VS Code session start (~100-300ms), so you always pick up new releases.

⚠️ Restart your AI client after editing the config. MCP servers are only picked up on client startup.

3. Try it — 30 seconds to verify it works

Paste this in your AI chat:

"Remember that my favourite TypeScript framework is Hono, and please call memory_write to save it."

Your agent should call memory_write and confirm the memory was stored.

Then open a new chat / new session (this is the whole point — memory survives restarts), and ask:

"What's my favourite TypeScript framework?"

Your agent should call memory_read, find the entry, and answer "Hono". If it does — you're wired up. Write whatever you want next.

If it doesn't remember: check that the client was fully restarted and the config has your real mk_live_... key, not the placeholder.

Tools

Tool	What it does
`memory_write`	Store a memory — insight, preference, lesson learned
`memory_read`	Search memories by natural language query
`memory_feedback`	Rate memories as helpful or not (improves future recall)
`memory_stats`	Check how many memories stored, which domains exist
`memory_delete`	Permanently delete a single memory by `atom_id`
`memory_delete_domain`	Wipe an entire domain (requires `confirm: true` safety interlock)

Ideas: What to Remember

User preferences: "I use dark mode", "I prefer Tailwind over CSS modules"
Project context: "This project uses PostgreSQL + Prisma", "Deploy to Railway"
Lessons learned: "Always run tests before push on this repo"
Decisions made: "We chose REST over GraphQL because of caching simplicity"
People & roles: "Alice is the designer, Bob owns the API"
Past mistakes: "Don't deploy on Fridays — learned this the hard way"

Universal Memory

The same API key works across all tools. Write a memory in Claude Code — read it in Cursor. Learn something in VS Code — your GPT Custom Action knows it too.

                    ┌── Claude Code (this MCP server)
                    ├── Cursor (this MCP server)
   Mnemoverse API ──├── VS Code (this MCP server)
   (one memory)     ├── GPT (Custom Actions)
                    ├── Python SDK (pip install mnemoverse)
                    └── REST API (curl)

Configuration

Env Variable	Required	Default
`MNEMOVERSE_API_KEY`	Yes	—
`MNEMOVERSE_API_URL`	No	`https://core.mnemoverse.com/api/v1`

Privacy

This server sends only what you explicitly choose to store or search to the Mnemoverse API (core.mnemoverse.com), authenticated with your API key. It does not read your AI client's conversation history, your local files, or anything you don't pass to a memory_* tool. Stored memories live under your account and are never sold or shared with third parties.


Privacy Policy	https://mnemoverse.com/privacy.html
Data sent	the `content` / `concepts` / `domain` you pass to `memory_write`; the `query` you pass to `memory_read`
Retention & deletion	delete one memory with `memory_delete`, or an entire namespace with `memory_delete_domain`
Contact	hello@mnemoverse.com