Context7
Up-to-date docs for AI agents
作者による説明(英語の原文)
Local-first memory & context-handoff across AI tools — capture in one, resume in another. No cloud.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"mindmap": {
"command": "npx",
"args": [
"-y",
"@ravi-labs/mindmap-mcp-server"
]
}
}
}ターミナルでこれを実行します
claude mcp add mindmap -- npx -y @ravi-labs/mindmap-mcp-serverプロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"mindmap": {
"command": "npx",
"args": [
"-y",
"@ravi-labs/mindmap-mcp-server"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"mindmap": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@ravi-labs/mindmap-mcp-server"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント ravi-labs で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.5.0)がレジストリに届いたのは4 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにNode.jsも必要です。
同じカテゴリには:Context7, Codebase Memory, Exa
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
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
~/.mindmapthat 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._
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.
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" } }
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
Just talk naturally inside your AI tool — the model calls the right tool:
Run npx @ravi-labs/mindmap-mcp-server quickstart for the full getting-started guide.
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.
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:
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.
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_captureto save the context.
When I reference past work, call
mindmap_resumefirst.
The tools are also described to encourage the model to do this proactively.
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:
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.
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.
mindmap_persona_set. High confidence, yours, editable.mindmap_persona_learn derives recurring signals from your existing memories. No LLM needed (keyword heuristic); richer if you enable one.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_personaand apply it. When I state a
durable preference, call
mindmap_persona_set.
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.
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冒頭だけを表示しています。続きはリポジトリで読めます