Vexa
Meeting bot and transcripts for Google Meet, Teams and Zoom. Live or after, speakers labelled.
作者提供的说明(英文原文)
A personal RAG database you build from chat, so AI creates work that sounds like you.
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 …”。
适用于网页版、桌面版和手机版 Claude:
https://myaitwin.lutolearn.com/mcp在终端中运行这条命令
claude mcp add --transport http myaitwin https://myaitwin.lutolearn.com/mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"myaitwin": {
"url": "https://myaitwin.lutolearn.com/mcp"
}
}
}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": {
"myaitwin": {
"type": "http",
"url": "https://myaitwin.lutolearn.com/mcp"
}
}
}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。
ChatGPT 只能连接互联网上的服务器:
https://myaitwin.lutolearn.com/mcp菜单名称可能因套餐和版本不同而略有差异。
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。
我们还没有读取这个服务器的工具列表;我们会轮流检查各个端点。
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8日
发布者(lutolearn.com)不是仓库所有者(LutoLabs);可能是其他人代为上架的
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
尚未检查;我们会轮流检查各个端点
尚未检查;我们会轮流检查各个端点
尚未检查;我们会在检查端点时读取工具列表
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 0 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。
它在作者的云端运行。通过互联网连接,无需安装,但你发送的内容会经过作者的服务器。
GitHub 仓库最后更新于–,最新版本(1.0.0)于6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。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。
同一分类中有:Vexa, emailmd, MCP WhatsApp Cloud
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
Meeting bot and transcripts for Google Meet, Teams and Zoom. Live or after, speakers labelled.
Render markdown into email-safe HTML, lint drafts for deliverability problems, and preview emails.
MCP server for WhatsApp Cloud API (Meta) — messages, media, and templates via Graph API
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。
A personal RAG database and semantic search engine you build and control from inside your AI chat. Store your knowledge, voice, and skills as you work. Retrieve them in seconds, source always cited. Your AI then creates output that is recognisably you, in every conversation.
Live at https://myaitwin.lutolearn.com. Free during early access.
MyAITwin is two things at once.
The toolbox. A production-grade RAG database with semantic search that you shape from chat. You define the structure, the types, the tags. It is yours, it is visible, and you are the architect of it.
The twin. The layer on top that greets you, guides you, assesses what you store, and creates output that sounds like you. It knows the difference between what you know (your knowledge) and how you say things (your skills), and it uses both.
Three steps. Under two minutes.
/create.Or use the canonical OAuth-authenticated endpoint:
https://myaitwin.lutolearn.com/mcpRequires a client with MCP capability. Currently Claude Pro, Claude Team, and ChatGPT Pro.
Storing knowledge
| Tool | What it does |
|---|---|
add_knowledge | Store a typed, tagged knowledge item |
add_voice_note | Store a voice note transcript with automatic extraction |
add_document | Store a long document with automatic chunking |
add_from_url | Fetch and store a web page |
add_reference_record | Store a creation event linking knowledge and skills used |
Retrieving knowledge
| Tool | What it does |
|---|---|
search_twin | Semantic search across all knowledge |
search_for_creation | Dual search returning skills and knowledge separately |
get_by_type | Retrieve all items of a specific type |
get_by_tag | Retrieve all items with a specific tag |
list_recent | List recently added items |
Understanding your twin
| Tool | What it does |
|---|---|
get_schema | Overview of your types and how many items you have |
get_sources | List all source documents |
find_patterns | Surface recurring patterns across your knowledge |
synthesise | Synthesise across multiple knowledge items on a topic |
Managing your twin
| Tool | What it does |
|---|---|
get_welcome | Session initialisation and system prompt |
update_knowledge | Update an existing item |
add_schema_type | Define a new knowledge type |
update_schema_type | Update an existing type definition |
delete_knowledge | Delete an item (destructive) |
All tools are annotated with title, readOnlyHint, and destructiveHint per the MCP spec. Of the 19: 10 read-only, 8 write (non-destructive), 1 destructive (delete_knowledge).
RAG is Retrieval-Augmented Generation. It is the architecture that lets AI answer using your specific knowledge rather than its training data alone.
Two layers:
When you search, both layers work together and return results ranked by relevance. Every result is cited with source and date, and tagged with provenance: personal (your own thinking), organisational (from your organisation), or external (from someone else).
The architectural insight worth getting right:
Knowledge is what you know. Facts, decisions, transcripts, observations.
Skills are how you express things. Your LinkedIn voice. Your email style. Your proposal structure.
Exceptional output needs both. Take a meeting transcript and ask for a follow-up email. The twin needs the transcript and your email skill to produce something that is accurate and unmistakably yours. Neither alone is enough.
/create. Deletion is immediate and irreversible.Privacy policy: https://myaitwin.lutolearn.com/privacy Security contact: security@lutolearning.com Privacy contact: privacy@lutolearning.com
com.lutolearn/myaitwinMIT. See LICENSE.
*MyAITwin MCP by Luto.*