Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Beschreibung vom Entwickler (englisches Original)
Create your digital twin: a public page that answers from your own material, with citations.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Funktioniert in Claude im Web, auf dem Desktop und auf dem Handy:
https://app.mindola.ai/mcp/lensFühre das in deinem Terminal aus
claude mcp add --transport http lens https://app.mindola.ai/mcp/lensIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"lens": {
"url": "https://app.mindola.ai/mcp/lens"
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"lens": {
"type": "http",
"url": "https://app.mindola.ai/mcp/lens"
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
ChatGPT verbindet sich nur mit Servern im Internet:
https://app.mindola.ai/mcp/lensMenünamen können sich je nach Tarif und mit der Zeit leicht unterscheiden.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Die Tool-Liste dieses Servers haben wir noch nicht gelesen; wir prüfen die Endpunkte reihum.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde über die Domain mindola.ai bestätigt (die Registry prüft, wem die Domain gehört)
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Noch nicht geprüft; wir prüfen die Endpunkte reihum
Noch nicht geprüft; wir prüfen die Endpunkte reihum
Noch nicht geprüft; wir lesen die Tool-Liste, wenn wir den Endpunkt prüfen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft in der Cloud des Entwicklers. Du verbindest dich übers Internet, ohne etwas zu installieren, aber was du sendest, läuft über die Server des Entwicklers.
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (1.0.2) kam vor 3 Monaten in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Er verbindet sich per URL, also funktionieren auch Apps, die Remote-MCP-Server akzeptieren, zum Beispiel ChatGPT im Entwicklermodus.
In derselben Kategorie: Spec Driven Development, Remote Desktop Commander, AvaHR
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
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.
Read hiring data and create inactive assessment and questionnaire drafts for human review.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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
Nur der Anfang wird gezeigt; den Rest findest du im Repo