Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Create your digital twin: a public page that answers from your own material, with citations.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Funciona en Claude para web, escritorio y móvil:
https://app.mindola.ai/mcp/lensEjecuta esto en tu terminal
claude mcp add --transport http lens https://app.mindola.ai/mcp/lensPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"lens": {
"url": "https://app.mindola.ai/mcp/lens"
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"lens": {
"type": "http",
"url": "https://app.mindola.ai/mcp/lens"
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
ChatGPT solo se conecta a servidores en internet:
https://app.mindola.ai/mcp/lensLos nombres de los menús pueden variar un poco según el plan y con el tiempo.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Todavía no hemos leído la lista de herramientas de este servidor; revisamos los endpoints por turnos.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026
El nombre en el registro se verificó con el dominio mindola.ai (el registro comprueba quién es dueño del dominio)
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Aún sin revisar; revisamos los endpoints por turnos
Aún sin revisar; revisamos los endpoints por turnos
Aún sin revisar; leemos la lista de herramientas cuando revisamos el endpoint
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en la nube del creador. Te conectas por internet sin instalar nada, pero lo que envías pasa por los servidores del creador.
El repo de GitHub cambió por última vez –, y la última versión (1.0.2) llegó al registro hace 3 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. Se conecta por URL, así que también funcionan las apps que aceptan servidores MCP remotos, como ChatGPT en modo desarrollador.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, Draugr
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
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.
Security scanning for AI agents: SAST, SCA, secrets, IaC, DAST, ranked by real risk.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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
Solo se muestra el principio; lee el resto en el repo