原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Sigao Li — personal MCP server

作者:SigaoLi版本 1.0.0i★ –i在云端运行
注册名称com.sigaoli/profile

安装

概览

作者提供的说明(英文原文)

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

PopNx MCP 的三大支柱

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。

它能帮你做什么

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。

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

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:

  1. 打开 Settings → Connectors
  2. 点击 Add custom connector
  3. 粘贴下面的地址,然后点击 Add
  4. 如果要求登录,请登录作者的服务
SettingsConnectors1+ Add2https://…3
https://api.sigaoli.com/mcp

在终端中运行这条命令

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

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "profile": {
      "url": "https://api.sigaoli.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。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

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

  1. 打开网页版 ChatGPT → Settings → Connectors (Apps)
  2. 点击 Create 或 Add(部分套餐需要先在 Advanced 中开启 Developer mode)
  3. 粘贴下面的地址,起个名字,然后保存
  4. 如果要求登录,请登录作者的服务
SettingsConnectors1+ Create2https://…3
https://api.sigaoli.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。

它能做什么

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。

基础检查i

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

  • ✓ 通过

    作者与仓库一致

    注册名称已通过域名 sigaoli.com 验证(注册中心会核实域名所有权)

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    端点有响应

    尚未检查;我们会轮流检查各个端点

  • … 尚未检查

    使用 OAuth 登录

    尚未检查;我们会轮流检查各个端点

  • … 尚未检查

    工具描述中没有隐藏指令

    尚未检查;我们会在检查端点时读取工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32599 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32599 个服务器的 21% 更新
  • 基础检查结果等于或优于此分类中其他 32599 个服务器的 100%

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。

常见问题

Sigao Li — personal MCP server 安全吗?

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。

它需要哪些密钥或代码?

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。

它在我的电脑上运行还是在云端?

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。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(1.0.0)于3个月前进入注册中心。

哪些应用可以使用它?

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。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, Zendesk

替代选择

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

Spec Driven Development

作者:formulahendry

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

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者:desktop-commander

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

在云端运行★ 211前天更新
安装

Zendesk

作者:pipeworx-io

Zendesk MCP Pack — tickets, users, organizations via OAuth.

电脑或云端今天更新
安装

作者的 README

打开 README

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。

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

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