Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Create your digital twin: a public page that answers from your own material, with citations.
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://app.mindola.ai/mcp/lens在终端中运行这条命令
claude mcp add --transport http lens https://app.mindola.ai/mcp/lens粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"lens": {
"url": "https://app.mindola.ai/mcp/lens"
}
}
}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": {
"lens": {
"type": "http",
"url": "https://app.mindola.ai/mcp/lens"
}
}
}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://app.mindola.ai/mcp/lens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7日
注册名称已通过域名 mindola.ai 验证(注册中心会核实域名所有权)
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 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(1.0.2)于3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:Spec Driven Development, Remote Desktop Commander, Agent Data Tools: Utilities
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
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
103 tools: JSON repair, CSV, hashes, cron, dates, units, validators. 25 free calls.
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。
Mindola turns a person's own material (writing, notes, documents, links) into a public AI page that answers questions from that material alone, with citations.
That public AI page is called a lens: one shareable link, like https://mindola.ai/me/your-name, where anyone can ask questions in chat or a live voice call. Every answer is grounded only in the owner's material and shows its sources. If the material does not cover a question, the lens says so honestly instead of guessing, and can hand the question to the real person.
This repository is the public documentation and examples for Mindola's hosted MCP server. The server is remote: there is nothing to install or run. You add one URL to your MCP client (Claude, Cursor, VS Code, Codex, ChatGPT), and your AI assistant can create and fill a lens for you in one conversation. The server itself runs inside the Mindola app, which is closed source; this repo holds the docs and example code.
Try a live lens right now, no account needed: mindola.ai/me/charles-darwin.
People publish knowledge that nobody reads. Your blog posts, documentation, course material, and notes sit in archives, and visitors are expected to dig through them. Most will not. They have one question and they want one answer.
A generic AI chatbot is not the fix. It answers from whatever it was trained on, so it confidently makes things up about you, your product, or your work. That is worse than no answer.
Mindola sits in between. It gives your material a front door: a page that answers questions from your knowledge base and nothing else, cites where each answer came from, and admits when it does not know. Visitors get answers instead of archives, and you get to see what they asked.
Building retrieval-augmented generation (RAG) yourself is a real project. You need ingestion for every source type, chunking that respects document structure, embeddings, a vector store, retrieval tuning, grounding logic so the model stays inside your material, a citation UI, refusal behavior for out-of-scope questions, and then hosting, monitoring, and a front end. Add real-time voice and it roughly doubles.
Mindola gives you the finished product behind one link and one API. You provide the material; Mindola handles knowledge retrieval, grounded answers, citations, refusals, voice, the public page, and the inbox for unanswered questions. You can go from nothing to a working grounded AI page in one conversation with your coding assistant.
Building your own is still the right call in some situations. If you need full control over which models run, where your data lives, or how retrieval is ranked, or you want to embed the pipeline deep inside an existing product, roll your own. Mindola is for everyone who wants the outcome without owning the pipeline.
The MCP server is hosted. There is nothing to run locally; you point your client at a URL.
Claude Code
claude mcp add --transport http mindola https://app.mindola.ai/mcp/lens
Cursor (~/.cursor/mcp.json or project .cursor/mcp.json)
{
"mcpServers": { "mindola": { "url": "https://app.mindola.ai/mcp/lens" } } }
Claude Desktop / claude.ai
Settings -> Connectors -> Add custom connector -> URL https://app.mindola.ai/mcp/lens.
For VS Code, Codex CLI, Windsurf, Gemini CLI, ChatGPT, and troubleshooting, see docs/installation.md. Ready-to-copy client configs live in examples/clients/.
Create a Mindola lens for me. I'm a nutrition coach specializing in
plant-based diets for endurance athletes. I've been coaching for 8 years. Use a warm tone. Ask me anything you need, then show me the profile before you create it.
create_digital_twin_lens and returns:No account is needed to create. The lens starts out unclaimed: live at its share URL, expiring after 30 days if never claimed. While it is unclaimed, your assistant can keep adding knowledge to it with add_lens_knowledge. Once you claim it, changes go through the app or the authenticated developer API.
Add my last three blog posts to the lens, then check the ingestion
status and tell me when everything is ready.
The assistant calls add_lens_knowledge with the URLs, then polls get_lens_status until the knowledge counts show everything as ready. Open the share URL and ask your lens a question.
Create an API key in the Mindola app, then create a lens seeded with knowledge in one call:
curl -s https://app.mindola.ai/api/v1/lenses \
-H "Authorization: Bearer mnd_your_api_key_here" \ -H "Content-Type: application/json" \ -d '{ "name": "Product docs assistant", "greeting": "Hi, ask me anything about the product.", "knowledge": [ { "type": "url", "content": "https://example.com/docs/getting-started", "title": "Getting started" }, { "type": "text", "content": "Refunds are available within 30 days of purchase.", "title": "Refund policy" } ] }'
The response is 201 with { id, token, shareUrl, sourcesQueued }. Ingestion runs in the background; poll GET /api/v1/lenses/:id until the knowledge counts show ready. Full reference in docs/api.md.
Ten of these are written out end to end in examples/use-cases/, each with a prompt to paste, the conversation to expect, and the final result.
Paste any of these into Claude, Codex, Cursor, or ChatGPT once the server is connected.
Create a Mindola lens for me. I'm a freelance UX designer with 10 years
of experience in fintech and healthcare. Interview me briefly to fill the gaps, show me the profile, and only create it after I approve.
Read the README and docs folder of this repository, then create a
Mindola lens that answers questions about the project's docs. Show me the proposed profile before creating anything.
Turn my CV into a Mindola lens that answers recruiters' questions.
Here is my CV: [paste CV]. Keep the tone professional and add a boundary that it never discusses salary expectations.
Create a Mindola lens for my online course "Intro to Data Analysis".
It should act as a course assistant for students. I'll paste the syllabus and lesson summaries next.
Add these three articles to my Mindola lens YOUR_LENS_TOKEN:
https://example.com/post-1 https://example.com/post-2 https://example.com/post-3
Check the status of my Mindola lens YOUR_LENS_TOKEN. Tell me how many
knowledge sources are ready, pending, or failed.
Create a Mindola lens for our startup's support. Use our FAQ page at
https://example.com/faq and the refund policy I paste below. Tone: professional. Add suggested prompts about setup, billing, and refunds.
Create a Mindola lens in Dutch (locale nl) for my bakery. It should
answer questions about opening hours, allergens, and custom orders from the text I provide.
One hundred more, organized by role and goal, in docs/prompts.md. For an industry-first view, examples/100-prompts.md holds 100 one-sentence prompts grouped by field, from customer support to online courses.
The REST developer API v1 lives at https://app.mindola.ai and authenticates with Authorization: Bearer mnd_ . Write endpoints are limited to 30 requests per minute per key.
| Endpoint | What it does |
|---|---|
POST /api/v1/lenses | Create a lens, optionally seeding up to 50 text or url knowledge items |
GET /api/v1/lenses | List your lenses, up to 100, newest first |
GET /api/v1/lenses/:id | Get one lens with its knowledge counts (total, ready, pending, failed) |
| POST /api/v1/lenses/:id/knowledge | Add up to 50 more knowledg
只显示了开头部分;其余内容请在仓库中阅读