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Mindmap MCP Server

제작자: ravi-labs버전 0.5.0i★ –i내 컴퓨터에서 실행
레지스트리 이름io.github.ravi-labs/mindmap-mcp-server

설치

개요

제작자가 쓴 설명(영어 원문)

Local-first memory & context-handoff across AI tools — capture in one, resume in another. No cloud.

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

  • 연결 방식: 내 컴퓨터에서 실행
  • 전송 방식: stdio
  • 카테고리: 검색 및 AI 기억

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

  • 기본 점검: 1개 중 1개 통과
  • 내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
  • 레지스트리에 입력해야 할 키가 나와 있지 않아요.

필요한 것

  • npmNode.js 설치(npx 명령어용)

어떤 앱을 쓰세요?

위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.

Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요

{
  "mcpServers": {
    "mindmap": {
      "command": "npx",
      "args": [
        "-y",
        "@ravi-labs/mindmap-mcp-server"
      ]
    }
  }
}

터미널에서 이 명령어를 실행하세요

claude mcp add mindmap -- npx -y @ravi-labs/mindmap-mcp-server

Cursor에서 열기

또는 설정을 직접 복사하기

프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요

{
  "mcpServers": {
    "mindmap": {
      "command": "npx",
      "args": [
        "-y",
        "@ravi-labs/mindmap-mcp-server"
      ]
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

VS Code에서 열기

또는 설정을 직접 복사하기

프로젝트의 .vscode/mcp.json에 붙여 넣으세요

{
  "servers": {
    "mindmap": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@ravi-labs/mindmap-mcp-server"
      ]
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.

선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.

이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.

할 수 있는 일

내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검

  • ✓ 통과

    제작자와 저장소 일치

    레지스트리 이름이 GitHub 계정 ravi-labs 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요

  • … 아직 점검 안 함

    계속 관리되고 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • … 아직 점검 안 함

    라이선스 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • – 해당 없음

    엔드포인트 응답

    내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요

  • – 해당 없음

    OAuth로 로그인

    내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요

  • … 아직 점검 안 함

    도구 설명에 숨겨진 지시 없음

    내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요

검색 및 AI 기억 카테고리의 다른 서버와 비교i

  • 이 카테고리의 다른 서버 7249개 중 0%보다 스타가 많아요
  • 이 카테고리의 다른 서버 7249개 중 19%보다 최근에 레지스트리에 공개됐어요
  • 기본 점검 결과가 이 카테고리의 다른 서버 7249개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

Mindmap MCP Server, 안전한가요?

해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.

어떤 키나 코드가 필요한가요?

레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.5.0)은 4개월 전 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: Context7, Codebase Memory, Exa

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Context7

제작자: upstash

Up-to-date docs for AI agents

컴퓨터 또는 클라우드★ 64,224MIT오늘 업데이트
설치

Codebase Memory

제작자: DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

내 컴퓨터에서 실행★ 48,148MIT20일 전 업데이트
설치

Exa

제작자: exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

클라우드에서 실행★ 5,213MIT지난달 업데이트
설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

🧠 Mind Map — MCP memory & context-handoff server

Ever lose the thread of a discussion across Claude Code, Claude Desktop, and your other AI tools? Mind Map is a small, local-first MCP server that acts as a shared memory layer across all your local MCP clients.

You capture context at the end of a session, and resume it in any other tool — no more re-explaining your project from scratch. Memory you stop using cools and decays gracefully into one-line traces (it's never silently deleted), so your shelf stays small and trustworthy instead of becoming another haystack.

Local-first by design. It runs on your machine; your memories are plain

files in ~/.mindmap that never leave your control. No account, no cloud, no

server to trust. Install once with npx @ravi-labs/mindmap-mcp-server install.

_ChatGPT and other cloud/web clients are out of scope for now — they can't

reach a local server without a public endpoint, which conflicts with

local-first. See Future._

The idea in one loop

  1. Capture (effortless) — at the end of a discussion, save a portable summary.
  2. Resume (promote-on-reuse) — pull it forward in a new session. The act of reusing it is what promotes it to *trusted* memory. Throwaway sessions never get promoted, so they never clutter your shelf.
  3. Consolidate (automatic) — a background pass cools unused memory through tiers (🔥 hot → 🌤️ warm → ❄️ cold) and collapses cold items to a searchable one-line trace. The moat isn't storing things — it's forgetting well.
  4. Tidy (opt-in, gamified) — a *cleanliness score* rewards pruning, not hoarding.

Install (one command)

Once published to npm, the whole install is:

npx @ravi-labs/mindmap-mcp-server install

This auto-detects your local MCP clients (Claude Desktop, Cursor, Windsurf) and writes the config for you, and configures Claude Code via its CLI if present. Restart your client and Mind Map's tools are there. Preview first with npx @ravi-labs/mindmap-mcp-server install --dry-run; undo with npx @ravi-labs/mindmap-mcp-server uninstall.

Data lives in ~/.mindmap/ by default — override with the MINDMAP_DIR env var.

Manual setup (if you prefer)

Every client uses the same command — npx -y @ravi-labs/mindmap-mcp-server over stdio.

Claude Code:

claude mcp add mindmap -- npx -y @ravi-labs/mindmap-mcp-server

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

{

"mcpServers": { "mindmap": { "command": "npx", "args": ["-y", "@ravi-labs/mindmap-mcp-server"] } } }

Cursor / Windsurf / other clients — same command + args shape in that client's MCP config. Add an env block to relocate data:

{ "command": "npx", "args": ["-y", "@ravi-labs/mindmap-mcp-server"],

"env": { "MINDMAP_DIR": "/path/to/my/memory" } }

From source (development)
git clone https://github.com/ravi-labs/mindmap-mcp-server.git && cd mindmap-mcp-server

npm install && npm run build node dist/index.js install --local # points clients at this checkout

Using it day to day

Just talk naturally inside your AI tool — the model calls the right tool:

  • "Save this to mind map" → captures the current context
  • "Resume my work on \ " → pulls it back into a fresh session
  • "What's in my mind map?" / "Show my mind map health" → browse / score
  • "What do you know about how I work?" → reads your persona (see below)
  • "Brainstorm \ with me" → loads your prior thinking so ideas continue across tools
  • "Add this to my Goals" / "park this for later" → files it into a collection
  • "What apps did I start and never finish?" → auto-organizes your memory into collections
  • "Continue one of my unfinished projects" → pick one, and you're back in the right folder at the exact next step

Run npx @ravi-labs/mindmap-mcp-server quickstart for the full getting-started guide.

Brainstorm across tools

Start an idea in Claude, keep going in Cursor — without re-explaining it. Mind Map is the shared memory; each tool brings its own brainstorming muscle. mindmap_brainstorm loads your persona and prior idea-threads on the topic, you brainstorm, and saving with kind: "brainstorm" lets it resume anywhere. A bundled brainstorm Skill (installed into ~/.claude/skills by install) wires the load → brainstorm → save flow for Claude.

Organize it your way — Collections

Mind Map always organizes automatically (recency tiers, source, topic, workspace). Collections are your own layer on top: named groups like 🎯 Goals, 🅿️ Parking Lot, ✅ Decisions — or anything you invent. Say *"add this to my Goals"* or *"park this"* in any tool, or use the dashboard's Organize tab.

Two things make collections more than folders:

  • Filing protects from decay. A memory in a pinned collection won't be forgotten while it's filed — parking something is a signal it matters. (An explicit "forget this" still wins.)
  • Every collection is a launcher. *"Continue one of my unfinished projects"* lists them — each with its 📂 workspace and *↳ where you left off* — pick one and mindmap_resume drops you back in: full topic context, right folder, exact next step.

Don't want to file things by hand? Auto-organize reads across *all* your sessions and proposes collections — it always looks for unfinished projects (started building, went cold, never shipped), Goals, Decisions, and parked ideas, plus anything you ask for (*"everything about billing"*). It only suggests; nothing is filed until you accept. Uses your own LLM if configured, otherwise a no-key heuristic.

Make capture automatic

For Claude Code, install adds a `SessionEnd` auto-capture hook — every substantive session is distilled into a memory automatically, so you never have to remember to save. It's deduped (resuming + re-ending updates in place) and skips trivial/automated sessions. Opt out with install --no-hook; it's removed cleanly by uninstall.

For other clients (or as a belt-and-braces nudge), add one line to your client's instructions (e.g. Claude Code's CLAUDE.md):

At the end of a substantive session, call mindmap_capture to save the context.

When I reference past work, call mindmap_resume first.

The tools are also described to encourage the model to do this proactively.

Bring in your past sessions

Import your existing Claude history into Mind Map — distilled, not raw dumps:

npx @ravi-labs/mindmap-mcp-server import --dry-run   # preview

npx @ravi-labs/mindmap-mcp-server import # apply

Covers Claude Code (CLI — full prompts), Cowork (title + opening message), VS Code Copilot (full prompts), and Cursor (chat titles + prompts); each memory is tagged by source. Filters: --source code|cowork|copilot|cursor, --project , --limit N. Imported memories keep their original dates, so old ones settle into cold traces automatically.

Imported memories capture the discussion (your prompts + the assistant's substantive answers), not just titles — for the transcript-backed sources (Claude Code, Cursor, Copilot). Re-run with --reimport to refresh existing memories in place after an upgrade.

Notes:

  • Cursor stores chats in a (often multi-GB) SQLite DB, read via Node's built-in SQLite — so Cursor import needs Node 22.5+ (other sources don't).
  • ChatGPT and Claude.ai web chats can't be imported — they live in the cloud, not local files. (A future "import from data-export file" is planned.)
Tidy up
npx @ravi-labs/mindmap-mcp-server cleanup --dry-run   # preview

npx @ravi-labs/mindmap-mcp-server cleanup # apply

Removes automated/scheduled-task memories and collapses duplicate sessions. Anything you've promoted is always kept.

Persona — a profile every tool can read

Beyond individual discussions, Mind Map keeps a persona: a distilled, evolving profile of *how you work* — your stack, style, communication preferences, and constraints — so any tool can apply it and stop re-asking the same setup questions. It's separate from your discussion memories.

  • Declared facts: tell a tool *"I prefer concise, code-first answers"* and it calls mindmap_persona_set. High confidence, yours, editable.
  • Inferred facts: mindmap_persona_learn derives recurring signals from your existing memories. No LLM needed (keyword heuristic); richer if you enable one.
  • Tools call mindmap_persona at the start of a session and apply what's there.

Add this to your client's instructions so it happens automatically:

At the start of a session call mindmap_persona and apply it. When I state a

durable preference, call mindmap_persona_set.

Own your context

A memory you can't extract, inspect, or carry isn't a memory you own. Three features make Mind Map's memory genuinely yours:

Memory Passport — export everything to one portable, open file, and pull your context *out of the walled gardens*:

npx @ravi-labs/mindmap-mcp-server passport export                 # → ~/mindmap-passport-<date>.json

npx @ravi-labs/mindmap-mcp-server passport import # from another machine npx @ravi-labs/mindmap-mcp-server passport import-chatgpt conversations.json # your ChatGPT export npx @ravi-labs/mindmap-mcp-server passport import-claude conversations.json # your Claude.ai export

The cloud chats can't be reached live from a local server — but their data export files are yours, and this imports them as distilled memories.

Glass-box memory — see exactly what's stored and why:

npx @ravi-labs/mindmap-mcp-server audit

Every memory shows its provenance (where it came from), trust (how many times you reused it), and decay forecast (when it fades to a one-line trace). In the dashboard, each memory has a one-click Forget. Unlike opaque vector stores, nothing about your memory is hidden from you.

Persona projection — write your persona into each tool's own config, so even non-MCP tools know how you work:

npx @ravi-labs/mindmap-mcp-server persona-sync          # Claude, Cursor, Copilot, Windsurf

It edits only a managed block ( …:end), so your own content is never touched.

Optional: bring your own LLM key

Mind Map runs fully without any LLM — every feature has a no-LLM path. If you *want* smarter persona inference and topic-graph labels, plug in your own provider (anthropic, openai, google, or local ollama). It's opt-in and graceful: no key, or a failed call, simply falls back to the no-LLM path, and any cost notes are rough estimates — never a bill.

Mind Map never stores your API key. It saves only the provider + model name in ~/.mindmap/llm.json; the key is read from an environment variable at call time. You set the key; Mind Map just reads it.

Smarter search (optional). Search is BM25 out of the box — no LLM needed. With an embeddings-capable provider (OpenAI, Google, or Ollama — Anthropic has no embeddings API), build a local embedding cache for hybrid search (BM25 fused with semantic similarity via reciprocal rank fusion):

npx @ravi-labs/mi

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