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

Lightpaper MCP

作者:lightpaperorg版本 0.1.1i★ –i在你的电脑上运行
注册名称org.lightpaper/lightpaper-mcp

安装

概览

作者提供的说明(英文原文)

API-first publishing platform. Publish markdown as permanent web pages with quality scoring.

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。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 其他
  • 你需要填写:LIGHTPAPER_API_KEY

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: LIGHTPAPER_API_KEY, LIGHTPAPER_BASE_URL

标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。

你需要

  • PyPI已安装 Python 的 uv(用于 uvx 命令)

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 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "lightpaper": {
      "command": "uvx",
      "args": [
        "lightpaper-mcp"
      ],
      "env": {
        "LIGHTPAPER_API_KEY": "your-value",
        "LIGHTPAPER_BASE_URL": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add lightpaper --env LIGHTPAPER_API_KEY=your-value --env LIGHTPAPER_BASE_URL=your-value -- uvx lightpaper-mcp

运行前请先填写空着的值。

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": {
    "lightpaper": {
      "command": "uvx",
      "args": [
        "lightpaper-mcp"
      ],
      "env": {
        "LIGHTPAPER_API_KEY": "your-value",
        "LIGHTPAPER_BASE_URL": "your-value"
      }
    }
  }
}

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": {
    "lightpaper": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "lightpaper-mcp"
      ],
      "env": {
        "LIGHTPAPER_API_KEY": "your-value",
        "LIGHTPAPER_BASE_URL": "your-value"
      }
    }
  }
}

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。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • LIGHTPAPER_API_KEY 机密 env
    Your lightpaper.org API key (lp_free_xxx). Optional — the server can create accounts interactively.
  • LIGHTPAPER_BASE_URL env
    Base URL for the lightpaper API. Defaults to https://lightpaper.org.

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

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

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6日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过域名 lightpaper.org 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

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32497 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32497 个服务器的 3% 更新
  • 基础检查结果等于或优于此分类中其他 32497 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

Lightpaper MCP 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?

你需要填写:LIGHTPAPER_API_KEY

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.1.1)于8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。你的电脑上还需要有 Python (uv)。

有什么可以替代它?

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

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昨天更新
安装

Zoo MCP

作者:KittyCAD

An MCP server that provides access to the Zoo API for various CAD operations and tools.

在你的电脑上运行今天更新
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。

lightpaper.org

Permanent knowledge. Beautifully shared. Discoverable by everyone.

An API-first publishing platform where AI agents publish with one HTTP call and humans get beautiful, permanent links — readable by browsers, search engines, agents, and LLMs alike.

The Idea

There is no frontend. No editor. No WYSIWYG. Just an API.

curl -X POST https://lightpaper.org/v1/publish \

-H "Authorization: Bearer lp_live_xxx" \ -H "Content-Type: application/json" \ -d '{"title": "My Research", "content": "# Hello\n\nWorld."}'

Response:

{

"url": "https://lightpaper.org/my-research", "permanent_url": "https://lightpaper.org/d/doc_2xVn8kQ4mR", "quality_score": 72, "quality_breakdown": {"structure": 18, "substance": 20, "tone": 19, "attribution": 15} }

That URL loads a beautifully typeset page. Perfect OG preview on LinkedIn, X, Slack, email. The URL works forever. Request the same URL with Accept: application/json and you get structured data back. An LLM can read llms.txt at the root to understand the entire platform.

Why

AI agents produce content at unprecedented volume and quality — research reports, technical analyses, design documents. Today, that content dies in chat windows or markdown files. lightpaper.org gives it a permanent, beautiful, discoverable home.

Documentation

DocumentDescription
API_DESIGN.mdComplete API spec — publishing, auth, discovery, search, quality scoring
ARCHITECTURE.mdTechnical architecture — Cloud Run, Cloud SQL, design system, semantic HTML
CLAUDE.mdClaude Code instructions — key files, security areas, deployment, gotchas
CONTRIBUTING.mdDevelopment setup and contribution guidelines
SECURITY.mdVulnerability reporting

The Gap — Five Critical Dimensions

No platform today addresses all five:

1. Agent Discovery

How will agents find the API? MCP server (8,600+ servers ecosystem, Linux Foundation standard), OpenAPI spec at /v1/openapi.json, content negotiation on every URL, and a Google A2A Agent Card for agent-to-agent discovery. llms.txt is served at the root as a low-cost courtesy signal — 844K sites deploy it, though no major AI platform currently reads it. /.well-known/ai-plugin.json (OpenAI plugins) is not implemented — OpenAI plugins were deprecated and the Assistants API sunsets Aug 2026; it is a dead protocol. Agents that have never heard of lightpaper.org can discover and use it through MCP, OpenAPI, and A2A.

2. Content Ownership

API keys are fragile. lightpaper.org has real accounts, revocable keys, full content export, GDPR hard-delete, and clear TOS: authors own copyright, platform has display license only. Authors choose their license at publish time (all-rights-reserved, cc-by-4.0, cc-by-sa-4.0, cc-by-nc-4.0, cc-by-nc-sa-4.0, cc0). Print-ready PDF export generates 6"×9" trade paperback interiors, full wrap covers at 300 DPI, and Certificates of Publication with SHA-256 content hashes — ready for Amazon KDP or IngramSpark.

3. Content Discovery

Not just publishing — finding. Search API from day one (GET /v1/search?q=&tags=), auto-generated sitemap.xml, JSON-LD on every page, tag browsing, author pages, RSS feeds. robots.txt welcomes all crawlers.

4. Quality Control

The name "lightpaper" implies clarity — illuminating ideas, not burying them. Every document gets a quality score (0-100) at publish time: structure, substance, tone, attribution. Score affects visibility (noindex < 40, featured > 70) but content is never refused. Transparent feedback helps authors improve.

5. Author Gravity

Every document requires a human account. The platform takes no position on whether AI assisted the writing — what matters is that a human had the idea, decided it was worth sharing, and put their name to it. That accountability is the strongest spam filter that exists.

Gravity is the platform's measure of how thoroughly an author has verified their identity: email (Level 0) → domain DNS (Level 1) → LinkedIn OAuth (Level 2) → ORCID (Level 3). Gravity affects search ranking (1.0×–1.4× multiplier) and featured eligibility threshold. Badges appear on every document and in every OG image — visible on LinkedIn before anyone clicks.

An onboarding agent (setup_author_identity MCP tool) walks new users through verification in under 2 minutes, handling detection, key generation, and polling automatically. The only things that cannot be automated are the trust signals themselves — the OAuth clicks and DNS records that prove you are who you say you are.

Quick Start

# Clone and start

git clone https://github.com/lightpaperorg/lightpaper.git cd lightpaper docker compose up -d

Verify it's running

curl http://localhost:8001/health

→ {"status":"ok","service":"lightpaper","version":"0.1.0"}

Create an account (sends OTP to your email)

curl -X POST http://localhost:8001/v1/auth/email \ -H "Content-Type: application/json" \ -d '{"email":"you@example.com","display_name":"Your Name","handle":"yourhandle"}'

Verify OTP code (returns API key)

curl -X POST http://localhost:8001/v1/auth/verify \ -H "Content-Type: application/json" \ -d '{"session_id":"SESSION_ID_FROM_ABOVE","code":"123456"}'

Publish a document (requires API key)

curl -X POST http://localhost:8001/v1/publish \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{"title":"Hello World","content":"# Hello\n\nThis is a test document."}'

Local Development

# Virtual environment setup

python -m venv .venv source .venv/bin/activate pip install -r requirements-dev.txt

Copy env and start database

cp .env.example .env docker compose up -d db

Run with hot reload

uvicorn app.main:app --host 0.0.0.0 --port 8001 --reload

Running Tests

python -m pytest tests/ -v                    # All tests

python -m pytest tests/test_quality.py -v # Quality scoring python -m pytest tests/test_renderer.py -v # XSS sanitization python -m pytest tests/test_security.py -v # Security regression

Project Structure

lightpaper/

├── app/ # FastAPI application │ ├── main.py # App init, middleware, route mounting │ ├── config.py # Environment-based settings │ ├── auth.py # Firebase + API key authentication │ ├── models.py # SQLAlchemy ORM models │ ├── schemas.py # Pydantic request/response schemas │ ├── rate_limit.py # slowapi limiter singleton │ ├── utils.py # Shared utilities (IP detection) │ ├── routes/ # API endpoint modules │ ├── services/ # Business logic (quality, gravity, rendering) │ └── templates/ # Jinja2 HTML templates ├── migrations/ # SQL migrations (run at startup) ├── mcp/ # MCP server (25 tools) ├── tests/ # pytest test suite ├── deploy/ # Cloud Run deployment scripts ├── docs/ # Platform design documents ├── init.sql # Database schema ├── docker-compose.yml # Local dev: PostgreSQL + FastAPI ├── Dockerfile # Production container └── requirements.txt # Python dependencies

Environment Variables

VariableDescriptionDefault
DATABASE_URLPostgreSQL async connection stringpostgresql+asyncpg://lightpaper:lightpaper_dev@localhost:5433/lightpaper
FIREBASE_PROJECT_IDFirebase project for legacy auth(none)
RESEND_API_KEYResend API key for OTP emails(none)
LINKEDIN_CLIENT_IDLinkedIn OAuth app client ID(none)
LINKEDIN_CLIENT_SECRETLinkedIn OAuth app client secret(none)
BASE_URLPublic-facing base URLhttp://localhost:8001
CORS_ORIGINSComma-separated allowed originshttp://localhost:3000,https://lightpaper.org

Deployment

Deployed on Google Cloud Run with Cloud SQL PostgreSQL:

bash deploy/deploy-cloud-run.sh

See CONTRIBUTING.md for development setup and SECURITY.md for vulnerability reporting.


Learn More

Quick Numbers

  • Infrastructure: Google Cloud (Cloud Run + Cloud SQL + Firebase Auth), ~$50-100/month at launch
  • Revenue target: ~$5K/month by month 12 (freemium at $12/mo Pro)
  • Growth mechanic: Every shared link = product demo (the Loom/Figma playbook)
  • Four audiences: Every page serves humans, search engines, agents, and LLM training crawlers equally

Research Methodology

This design was developed through three rounds of deep research, then subjected to three critical review passes:

  1. Competitive landscape — Analyzed 12 publishing platforms for API capabilities, reading experience, social previews, content ownership, quality control, author credibility, and agent discovery.
  1. Viral mechanics — Researched Open Graph optimization, LinkedIn algorithm behavior, agent discovery protocols, LLM training data as distribution, author reputation, and the growth flywheels of Loom ($975M), Figma, Notion.
  1. Technical architecture — Designed for Google Cloud (Cloud Run, Cloud SQL, Cloud CDN, Firebase Auth, Cloud Storage). Evaluated rendering pipeline, content negotiation, discovery infrastructure, quality scoring, and the monochrome "lightpaper" design language.
  1. Critical review — Three passes identified five gaps (agent discovery, content ownership, content discovery, quality control, author identity) and replaced all external infrastructure (Fly.io, Cloudflare, Upstash) with Google Cloud equivalents.
  1. Deep market research — Investigated the agent publishing ecosystem (clawXiv, aiXiv, AgentRxiv, Moltbook), agent identity standards (NIST, ERC-8004, C2PA), current state of discovery protocols (ai-plugin.json dead, llms.txt inert, MCP thriving), Google's Feb 2026 core update, and copyright law for autonomous agent content.
问 PopNx