servemd 1.3.0

Beautiful markdown documentation server for humans and AI with native llms.txt and MCP (Model Context Protocol) support

servemd

Serve docs to humans and AI.

Beautiful markdown documentation with native llms.txt support. Zero configuration, production-ready.

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Why servemd?

Unlike basic markdown servers, servemd is built for the AI era:

Markdown → Beautiful HTML    → Humans
         → llms.txt          → AI/LLMs
         → llms-full.txt     → Complete AI context
         → /mcp endpoint     → AI assistants (250x less context)

For humans: Nuxt UI-inspired design, three-column layout, zero configuration. For AI: Native llms.txt support, structured context, ready for the Model Context Protocol era.

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✨ Features

• 🎨 Beautiful Design — Nuxt UI-inspired three-column layout (sidebar, content, TOC)
• 🤖 AI-Native — Built-in llms.txt and llms-full.txt for Claude, ChatGPT, Cursor, etc.
• 🔌 MCP Support — Model Context Protocol endpoint for interactive AI queries (250x less context)
• ✨ Zero Configuration — Drop .md files and go
• ⚡ Fast — Smart disk caching, <5ms cached responses
• 🐳 Docker Ready — Production-optimized container
• 🧪 Well Tested — 208 tests, 100% passing
• 📱 Responsive — Mobile, tablet, and desktop support

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🚀 Quick Start

Install

pip install servemd

Run

# Serve docs from current directory
servemd

# Or specify a directory
servemd ./my-docs

Visit http://localhost:8080 — your documentation is live.

Live demo: https://servemd.me.cloudns.cl

Alternative: Docker

# Quick start - mount your docs
docker run -p 8080:8080 -v $(pwd)/docs:/app/__docs__ jberends/servemd:latest

# Or build custom image with docs baked in
FROM jberends/servemd:latest
COPY ./my-docs/ /app/__docs__/

See DOCKER_README.md for complete Docker usage guide.

Alternative: uvx (no install)

uvx servemd ./my-docs

📚 Complete Setup Guide →

For Contributors

git clone https://github.com/servemd/servemd
cd servemd
uv sync
uv run python -m docs_server

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🤖 AI-Native: llms.txt & MCP Support

servemd automatically serves your docs in AI-friendly formats:

Endpoint	Purpose	Audience	Context Size
/{page}.html	Rendered HTML with navigation	Humans	N/A
/{page}.md	Raw markdown	AI/LLMs	Per-page
/llms.txt	Documentation index	AI assistants	Small (~5KB)
/llms-full.txt	Complete context (all pages)	AI deep context	Large (~500KB+)
/mcp	Interactive queries	AI (MCP clients)	Minimal (250x less)

llms.txt - Static Index

Example: Give an AI assistant your docs:

"Read my documentation at https://docs.example.com/llms.txt"

The AI gets a structured index with absolute URLs to every page. For complete context, use /llms-full.txt which includes all page content inline.

MCP - Interactive Queries (Recommended for AI)

Model Context Protocol provides on-demand documentation access with 250x less context:

POST /mcp
{
  "method": "tools/call",
  "params": {
    "name": "search_docs",
    "arguments": {
      "query": "authentication",
      "limit": 5
    }
  }
}

Benefits:

• 🎯 Precise — AI queries only what it needs
• ⚡ Fast — No sending entire documentation every time
• 💰 Cost-effective — 250x less tokens than llms-full.txt
• 🔍 Smart — Full-text search with Whoosh

Available MCP Tools:

• search_docs — Semantic search across documentation
• get_doc_page — Retrieve specific pages with section filtering
• list_doc_pages — List all available pages by category

See MCP Integration Guide for details.

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✨ Key Features

For Humans

• 🎨 Nuxt UI-inspired three-column layout (sidebar, content, TOC)
• 🎨 Syntax highlighting with Pygments
• 🎨 Responsive design (mobile, tablet, desktop)
• 🎨 Dark mode ready
• ✅ Tables, task lists, footnotes, Mermaid diagrams

For AI

• 🤖 llms.txt — Structured documentation index (5KB)
• 🤖 llms-full.txt — Complete context export (500KB+)
• 🔌 MCP endpoint — Interactive queries via Model Context Protocol
  • 250x less context than llms-full.txt
  • Full-text search with Whoosh
  • On-demand page retrieval
  • Section filtering
• 🤖 Automatic link transformation to absolute URLs
• 🤖 Curated or auto-generated indexes

For Developers

• ⚡ Fast — disk caching, <5ms cached responses
• 🔥 Hot reload in debug mode
• 🔧 Zero configuration required
• 🐍 Python 3.11-3.14, FastAPI, Pydantic
• 🧪 208 tests, 100% passing

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📁 File Structure

Your documentation needs just 3 required files:

docs/
├── index.md       # Homepage (required)
├── sidebar.md     # Navigation (required)
├── topbar.md      # Top bar (required)
├── llms.txt       # AI index (optional)
└── your-content.md # Your pages

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⚙️ Configuration

Configure via environment variables:

DOCS_ROOT=./docs                 # Documentation directory
CACHE_ROOT=./__cache__           # Cache directory
PORT=8080                        # Server port
DEBUG=true                       # Enable debug mode
BASE_URL=https://docs.site.com   # Base URL for llms.txt
MCP_ENABLED=true                 # Enable MCP endpoint (default: true)
MCP_RATE_LIMIT_REQUESTS=120      # MCP rate limit (requests per window)
MCP_RATE_LIMIT_WINDOW=60         # MCP rate limit window (seconds)

See Configuration Guide for details.

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🎯 Use Cases

servemd is perfect for:

• SaaS Documentation — Customer-facing support docs with AI assistant integration
• Open Source Projects — Self-hosted, beautiful docs
• Internal Teams — Company knowledge bases and wikis
• API Documentation — REST/GraphQL API docs
• Technical Writing — Blogs and tutorials

📘 Deployment

Method	Best For
Local Development	Development, previewing
Docker	Production, CI/CD
Cloud Platforms	Heroku, Railway, Fly.io, DigitalOcean
Kubernetes	k8s, k3s, Helm charts

🛠️ Examples

Check examples/ for ready-to-use templates:

• Dockerfile.user-template — Custom Docker image
• docker-compose.user.yml — Docker Compose setup
• k8s-simple.yaml — Kubernetes deployment

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🏗️ Architecture

Clean, modular FastAPI application:

src/docs_server/
├── config.py           # Settings & environment
├── helpers.py          # Utilities & navigation
├── caching.py          # Smart caching
├── markdown_service.py # Markdown rendering
├── llms_service.py     # LLMs.txt generation
├── templates.py        # HTML templates
├── main.py             # FastAPI routes
└── mcp/                # Model Context Protocol
    ├── server.py       # MCP JSON-RPC handler
    ├── tools.py        # MCP tools (search, get, list)
    ├── search.py       # Full-text search with Whoosh
    └── indexer.py      # Documentation indexing

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🧪 Testing

uv run pytest tests/ -v

# 208 tests, 100% passing ✅

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🔧 Development

git clone https://github.com/servemd/servemd
cd servemd
uv sync --group dev
uv run pytest tests/ -v
DEBUG=true uv run python -m docs_server

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📊 Performance

Endpoint	First Request	Cached
Rendered HTML	50-100ms	<5ms
Raw Markdown	<10ms	<10ms
LLMs.txt	100-200ms	<5ms

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📖 Documentation

• Quick Setup — Get running in 5 minutes
• Markdown Features — Tables, code blocks, diagrams
• LLMs.txt Guide — AI assistant integration
• MCP Integration — Interactive queries for LLMs
• Navigation — Sidebar and topbar configuration
• Configuration — Environment variables
• API Reference — HTTP endpoints

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🙋 Support

• 📖 Documentation: docs.servemd.dev or run locally
• 🚀 Live demo: servemd.me.cloudns.cl
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

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📜 License

MIT License — use freely for any project.

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🎉 Get Started Now

pip install servemd
servemd ./my-docs

Visit http://localhost:8080 — beautiful docs for humans, structured context for AI.

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servemd — Serve docs to humans and AI

Built with Python, FastAPI, and Markdown

Documentation · Live Demo · PyPI · GitHub