프로토타입 · 미리 보기: 공식 레지스트리의 모든 서버를 담고 있으며 30분마다 동기화해요. 리뷰와 계정 기능은 다음 단계에서 열려요.
PopNx MCPAI를 쓰는 사람을 위한 MCP 서버 · 쉬운 말로 설명해요

Sigao Li — personal MCP server

제작자: SigaoLi버전 1.0.0i★ –i클라우드에서 실행
레지스트리 이름com.sigaoli/profile

설치

개요

제작자가 쓴 설명(영어 원문)

Read-only access to Sigao Li's profile, CV and case studies. Bilingual (EN/ZH).

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

  • 연결 방식: 클라우드에서 실행
  • 전송 방식: Streamable HTTP
  • 카테고리: 기타

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

  • 기본 점검: 1개 중 1개 통과
  • 제작자의 서버에서 실행돼요. 내가 요청한 내용은 제작자의 서버를 거쳐요.
  • 레지스트리에 입력해야 할 키가 나와 있지 않아요.

필요한 것

어떤 앱을 쓰세요?

위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.

웹, 데스크톱, 모바일의 Claude에서 쓸 수 있어요:

  1. Settings → Connectors 열기
  2. Add custom connector 누르기
  3. 아래 주소를 붙여 넣고 Add 누르기
  4. 로그인하라고 하면 제작자의 서비스에 로그인하기
SettingsConnectors1+ Add2https://…3
https://api.sigaoli.com/mcp

터미널에서 이 명령어를 실행하세요

claude mcp add --transport http profile https://api.sigaoli.com/mcp

Cursor에서 열기

또는 설정을 직접 복사하기

프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요

{
  "mcpServers": {
    "profile": {
      "url": "https://api.sigaoli.com/mcp"
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

VS Code에서 열기

또는 설정을 직접 복사하기

프로젝트의 .vscode/mcp.json에 붙여 넣으세요

{
  "servers": {
    "profile": {
      "type": "http",
      "url": "https://api.sigaoli.com/mcp"
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요:

  1. 웹에서 ChatGPT 열기 → Settings → Connectors (Apps)
  2. Create 또는 Add 누르기(일부 요금제는 먼저 Advanced에서 Developer mode를 켜야 해요)
  3. 아래 주소를 붙여 넣고 이름을 정한 뒤 저장하기
  4. 로그인하라고 하면 제작자의 서비스에 로그인하기
SettingsConnectors1+ Create2https://…3
https://api.sigaoli.com/mcp

메뉴 이름은 요금제나 시기에 따라 조금 다를 수 있어요.

선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.

이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.

할 수 있는 일

아직 이 서버의 도구 목록을 읽지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검

  • ✓ 통과

    제작자와 저장소 일치

    레지스트리 이름이 도메인 sigaoli.com 기준으로 확인되었어요(레지스트리가 도메인 소유를 확인해요)

  • … 아직 점검 안 함

    계속 관리되고 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • … 아직 점검 안 함

    라이선스 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • … 아직 점검 안 함

    엔드포인트 응답

    아직 점검하지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요

  • … 아직 점검 안 함

    OAuth로 로그인

    아직 점검하지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요

  • … 아직 점검 안 함

    도구 설명에 숨겨진 지시 없음

    아직 점검하지 않았어요. 엔드포인트를 점검할 때 도구 목록을 읽어요

기타 카테고리의 다른 서버와 비교i

  • 이 카테고리의 다른 서버 32484개 중 0%보다 스타가 많아요
  • 이 카테고리의 다른 서버 32484개 중 22%보다 최근에 레지스트리에 공개됐어요
  • 기본 점검 결과가 이 카테고리의 다른 서버 32484개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

Sigao Li — personal MCP server, 안전한가요?

해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.

어떤 키나 코드가 필요한가요?

레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

제작자의 클라우드에서 실행돼요. 설치 없이 인터넷으로 연결하지만, 보내는 내용은 제작자의 서버를 거쳐요.

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(1.0.0)은 3개월 전 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. URL로 연결되기 때문에, 원격 MCP 서버를 지원하는 앱(예: 개발자 모드의 ChatGPT)에서도 쓸 수 있어요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, Recta

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Spec Driven Development

제작자: formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

내 컴퓨터에서 실행★ 440MIT14개월 전 업데이트
설치

Remote Desktop Commander

제작자: desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

클라우드에서 실행★ 211어제 업데이트
설치

Recta

제작자: RectaBuild

Cheaper cross-chain stablecoin routes for AI agents; 5 % of the verified saving, nothing if none.

컴퓨터 또는 클라우드오늘 업데이트
설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

sigaoli.com

Personal website of Sigao Li — AI Product Manager · Spatial Data Scientist. _From maps to models, and the products in between._

Bilingual (English at /, 中文 at /zh/), built with Astro + Tailwind CSS v4 + GSAP, deployed to GitHub Pages via GitHub Actions. Launched 2026-06-11, replacing the previous Jekyll (academicpages) site.

Highlights

  • Generative canvas effects on a map motif — an interactive particle field (home), contour terrain (work), a "river as timeline" with a flow field (CV), and a geo-network arc map (photography); all vanilla canvas/SVG, tuned to 60fps with reduced-motion and mobile fallbacks
  • Dotted world map — land sampled from Natural Earth, with 88 GPS-extracted photo footprints across 6 countries; click a marker to open that country's gallery
  • Zoe, the digital doorcat — Sigao's cat (驺虞) lives in the corner of every page as a set of AI-generated, chroma-keyed VP9-alpha video clips pinned to shared anchor poses, driven by a state machine: she dozes off when ignored, reacts to page switches, listens while you type, "types back" while the assistant streams, and keeps a few easter eggs (production handbook in docs/)
  • Built-in AI layer — a floating chat assistant (fronted by Zoe) on every page — it suggests the single most relevant page as you ask, and greets a returning visitor by name (stored only in their own browser, opt-in) — plus a personal MCP server, both fed by a build-time knowledge pack generated from the same sources as the pages (see below)
  • Machine-readable by design — /llms.txt, /llms-full.txt, /resume.json (JSON Resume), /knowledge.json, /.well-known/mcp.json, JSON-LD, and a robots.txt that explicitly welcomes AI crawlers
  • Photographs file themselves — drop the full-resolution originals into _inbox/ and commit. A pre-commit hook works out the country from the coordinates against Natural Earth polygons, archives the originals outside git, derives masters with a maximum edge of 4096px and the EXIF stripped, asks a vision model for the bilingual caption, and registers everything in photos.json — the map, the counts and the assistant's knowledge all follow from there
  • Either language pulls the other along — edit the Chinese or the English and commit. The hook works out which side moved and asks a model whether the edit actually changed what the other side must say. Polishing one language leaves the other alone; a changed fact is revised rather than rewritten, so hand-picked wording survives. Editing both in one commit counts as already handled by hand
  • Lighthouse (mobile): 96–100 across all categories; zero cookies, no paid services, and a plain-language privacy notice at /privacy

Commands

CommandAction
npm run devDev server at localhost:4321 (Astro 7 runs it as a daemon — stop with npx astro dev stop)
npm run buildProduction build to dist/
npm run previewServe the production build locally
npm testUnit tests for both hook pipelines (node --test, no extra dependencies)
npm run ingestPhoto ingest by hand. Normally the pre-commit hook runs it; useful for watching what it does
npm run syncBilingual sync by hand. Same — the hook runs it, this is for watching
node scripts/check-links.mjsInternal link integrity check over dist/
node scripts/verify-nav.mjs 等Playwright interaction suites (run against a local server)
npm run dev (in worker/)Chat + MCP Worker at localhost:8787 (wrangler; secrets in worker/.dev.vars, never committed)
node scripts/verify-chat.mjsE2E chat-widget test (needs both dev servers running)
node scripts/verify-zoe.mjsE2E for Zoe's action state machine (append ?zoe-fast locally to compress minute-scale timers)
node scripts/verify-typeroute.mjsE2E for the intent-driven typing clip and the bilingual 404 page

Any Playwright suite that waits on Zoe's state must pin the clock

(Date.prototype.getHours = () => 14): between 23:00 and 06:00 she starts the

session asleep, so state never reaches idle and the run just times out.

When adding a Zoe clip, decide who prewarms it and when at the same time.

A clip that is only fetched at playback stalls on a slow connection, and the

stage shows nothing until it decodes. Prewarming has been missed three times

already. Note warm() takes the *file* name (sit-to-loaf), not the ZOE

key (sitToLoaf).

The chat panel is rebuilt on every navigation — transition:persist keeps

Zoe's stage, not the panel. **Anything that lives only in panel DOM is gone

the moment a visitor clicks a link.** The streaming reply, the guidance chip

and the unsent draft each had to be given module state plus a path back

through paint(); the chip was lost for weeks before anyone noticed. So when

adding persistent UI here, answer two questions up front: how does paint()

rebuild it, and should it ride along in sessionStorage with the history?

Measure geometry only once the panel is visible — scrollHeight is 0 while

it is hidden, which silently writes height: 0px.

Turnstile guards `/chat` and `/classify`. It must never guard `/mcp`.

That endpoint exists so machines can read Sigao's profile — it is in the

official registry — and Turnstile exists to stop machines. It also costs

nothing to serve: the tools read the knowledge pack and never call a model.

The static outlets (llms.txt, knowledge.json, .well-known/mcp.json) are

served by Pages and never reach the Worker at all.

Locally, Turnstile uses Cloudflare's always-pass test keys — sitekey in

site.ts behind import.meta.env.DEV, secret in worker/.dev.vars. The real

key rejects headless browsers, which is exactly its job, so every suite that

drives a real Worker would fail against it. The real secret exists only in

production, set with wrangler secret put. A corollary worth remembering:

the production happy path cannot be verified from a script — reaching it

needs a human in a real browser. Automation can still prove the gate is up

(a request with no credential must return 403).

src/

├── pages/ # en routes + zh/ mirrors; llms.txt / resume.json / knowledge.json endpoints ├── components/ # Nav, Hero, WorldMap, Lightbox, CommandK, ChatWidget … │ └── pages/ # shared page bodies rendered by both locales ├── content/ # cases & research (en) + cases-zh & research-zh (generated, reviewed) ├── data/ # cv.json / cv.zh.json / photos.json (GPS + bilingual alts) │ └── knowledge/ # persona sources for the AI assistant (about / faq / guidelines / boundaries) ├── lib/ # i18n dict, GSAP lifecycle helper, site config │ └── knowledge/ # knowledge-pack pipeline (same-source layers + build-time privacy guard) └── assets/ # photo originals (optimized at build; originals never shipped) worker/ # Cloudflare Worker: /chat (SSE) + /classify (intent) + /mcp (MCP server) └── src/core/ # runtime-agnostic logic; Cloudflare specifics live only in src/adapter/ public/zoe/ # Zoe's clip library (600p VP9 alpha, lazy-loaded; idle loads first) docs/ # zoe-production-handbook.md — clip production specs & prompt cards

AI layer

One knowledge layer, three outlets: /llms-full.txt for passive crawlers, a chat assistant (POST /chat, SSE) for humans, and an MCP server (/mcp, Streamable HTTP, no auth — tools: get_profile / list_experience / get_case_study) for visiting agents, both served from api.sigaoli.com (Cloudflare Worker, code in worker/). The knowledge pack (/knowledge.json) is assembled at build time from the same sources as the pages — persona markdown, cv.json, case studies, photo stats — so any content edit propagates to all three outlets on the next deploy, no manual step. A privacy guard fails the build if sensitive patterns (phone numbers, IDs, coordinates) ever leak into the pack.

Alongside each reply the chat runs a lightweight intent classifier (POST /classify, a small model) to suggest the single most relevant page, and can remember a returning visitor's name — both kept entirely in the visitor's own browser (opt-in, clearable via "Forget me"), never on a server. Visitors in the EU/EEA/UK have their chat and classification routed to an EU-hosted provider, never the China-direct API. What the site stores and sends is described in plain language at /privacy.

Editing content

  • Case studies / research: edit either language and commit. The pre-commit hook works out which side moved, asks a model whether the edit actually changed what the other side must say, and only then revises it — passing the existing translation along so a changed fact gets fixed without the rest of your wording being rewritten. Polish one language and the other is left alone. Edit both in one commit and that counts as already handled by hand. See docs/bilingual-sync-design.md.
  • CV: edit src/data/cv.json or cv.zh.json — the hook keeps the other in step entry by entry, so changing one role doesn't touch the rest. The timeline, /resume.json and /llms-full.txt all render from it. Replace public/files/pdf/CV__Sigao_Li.pdf alongside. (skills is deliberately left out of the sync: the two languages use different shapes there and the renderer handles both.)
  • UI strings & hero copy: hand-written bilingual dictionary in src/lib/i18n.ts.
  • Photos: drop the full-resolution originals into _inbox/ and commit. The pre-commit hook files them by GPS, archives the originals to _originals/ (never committed), derives serving masters with a maximum edge of 4096px, writes bilingual captions with a vision model, and registers them in src/data/photos.json. Photos without GPS go in _inbox/ / instead. Counts, the map and the AI knowledge pack all follow from photos.json automatically. See docs/photo-ingest-design.md.
  • AI assistant persona: edit src/data/knowledge/*.md; the knowledge pack rebuilds on every deploy and the assistant follows within ~10 minutes (Worker-side cache TTL).
  • Zoe's actions: source clips live outside the repo; the pipeline (scripts/zoe-board2.mjs → zoe-qc2.mjs → zoe-prod2.mjs) keys, QCs, mirrors and encodes them into public/zoe/. New actions = one clip + one row in the ZOE table in ChatWidget.astro; specs and prompt cards in docs/zoe-production-handbook.md.

Local setup

Two chains run from a pre-commit hook: photo ingest, and bilingual sync. After cloning, point git at the tracked hooks directory once:

git config core.hooksPath .githooks

Without this neither runs — photos dropped into _inbox/ stay there, and the two languages stop tracking each other. Both exit immediately when there is nothing to do, so an ordinary commit is unaffected.

Both need .env (copy .env.example): the model calls for captions, translation and the staleness check all go through that gateway.

Regenerating the lock file. package-lock.json has to be built on Linux. npm records only the optional dependencies it can resolve on the machine it runs on, so a lock made on Windows omits what sharp needs on a runner and npm ci refuses to install. After changing dependencies here, run the Relock on Linux workflow from the Actions tab; it rebuilds the lock, proves npm ci works, and commits the result. Ordinary pushes never need it — and if you forget, the verify job says so.

Deployment

Push to master → deploy.yml builds and depl

앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요

PopNx에게 묻기