Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Local-first memory & context-handoff across AI tools — capture in one, resume in another. No cloud.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"mindmap": {
"command": "npx",
"args": [
"-y",
"@ravi-labs/mindmap-mcp-server"
]
}
}
}在终端中运行这条命令
claude mcp add mindmap -- npx -y @ravi-labs/mindmap-mcp-server粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"mindmap": {
"command": "npx",
"args": [
"-y",
"@ravi-labs/mindmap-mcp-server"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"mindmap": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@ravi-labs/mindmap-mcp-server"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 ravi-labs 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.5.0)于4个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Context7, Codebase Memory, Exa
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间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.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
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只显示了开头部分;其余内容请在仓库中阅读