Lightpaper Mcp

org.lightpaper由 lightpaper.org 發布v0.1.1更新於 Oct 27, 2026

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

概覽

AI 產生的概覽

lightpaper.org 的 MCP 伺服器,該平台以 API 為先,讓助理把 Markdown 發佈為永久、可分享的網頁並取得品質評分。

可以做什麼
  • 把 Markdown 文件發佈到 lightpaper.org,回傳永久連結、短連結以及品質評分與各項得分。
  • 搜尋已發佈文件,並依標籤、作者與訂閱來源瀏覽。
  • 建立並驗證作者帳號,包含產生 API 金鑰與身分驗證流程。
  • 匯出內容,包含可印刷的 PDF 輸出,並在發佈時選擇授權條款。
適用情境
適合把對話中產出的研究報告、技術分析或設計文件變成永久、可分享的網頁,而不是留在聊天視窗或本機 Markdown 檔案裡。也適合需要發佈內容能被瀏覽器、搜尋引擎和其他代理程式讀取的情境。
執行需求
以本機 Python 套件(lightpaper-mcp)透過 stdio 在桌上型電腦執行。需要 lightpaper.org 帳號,並透過 LIGHTPAPER_API_KEY 提供 API 金鑰;伺服器也可互動式建立帳號。LIGHTPAPER_BASE_URL 為選用,可指定其他 API 位址,預設指向託管服務。需要能連線至該平台的網路。
安裝前請注意
發佈是公開且大致永久的寫入:文件會提供給瀏覽器、爬蟲與 LLM,README 稱連結永久有效。API 金鑰是可代表帳號發佈內容的機密。帳號設定涉及電子郵件驗證以及選用的網域 DNS、LinkedIn OAuth 或 ORCID 身分驗證,這些身分訊號會公開展示。README 提到付費 Pro 方案。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Lightpaper Mcp,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

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.

bash
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:

json
{  "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

bash
# Clone and startgit clone https://github.com/lightpaperorg/lightpaper.gitcd lightpaperdocker compose up -d
# Verify it's runningcurl 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":"[email protected]","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

bash
# Virtual environment setuppython -m venv .venvsource .venv/bin/activatepip install -r requirements-dev.txt
# Copy env and start databasecp .env.example .envdocker compose up -d db
# Run with hot reloaduvicorn app.main:app --host 0.0.0.0 --port 8001 --reload

Running Tests

bash
python -m pytest tests/ -v                    # All testspython -m pytest tests/test_quality.py -v     # Quality scoringpython -m pytest tests/test_renderer.py -v    # XSS sanitizationpython -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
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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

來源:README.md,提交 35202b6

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

1
  1. v0.1.1最新Sep 16, 2026

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