Mockups MPC

A self-hosted gallery for AI-generated mockups — with an MCP interface. Instead of cluttering your repo, mockups get a permanent home and a clean web gallery you can browse. AI tools upload them over a direct curl call; MCP handles only the lightweight coordination — listing, metadata, tagging, and retrieval.

Token-efficient by design. MCP tool parameters flow through the model context, so sending a large HTML file via a tool call wastes tokens. Mockups MPC provides an HTTP upload endpoint (POST /api/upload) — the AI writes the file locally and curls it to the server, keeping file content entirely out of the model context. MCP tools handle lightweight operations only: listing, metadata, tagging, and deletion.

Prerequisites

• Docker (for deployment) or Python 3.12+ (for local development)

• An MCP-compatible AI client (Claude Code, Claude Desktop, etc.)

• Optional: Traefik reverse proxy (for production with TLS)

Related MCP server: LemGen AI Design MCP

Why

Every time an AI tool generates a mockup, there's no consistent place for it to go — so they pile up in your repo or get scattered across temp dirs, sessions lose track of them, and there's no history. Mockups MPC gives them a permanent home instead: the AI pushes the mockup to the gallery, you browse it there, and the local file gets cleaned up. One place for everything, nothing cluttering your project.

Architecture

┌─────────────────┐     MCP (HTTP/SSE)     ┌──────────────────────┐
│  Claude Code /  │ ◄───────────────────── │                      │
│  Claude Desktop │  send/list/get/update  │    Mockups MPC       │
│  Any MCP Client │  delete/tag            │    (FastAPI)         │
└─────────────────┘                        │                      │
                                           │  ┌────────────────┐  │
       Browser                             │  │  MCP Server    │  │
    ┌──────────┐      GET /                │  │  (fastmcp)     │  │
    │ Gallery  │ ◄──────────────────────── │  └────────────────┘  │
    │ Viewer   │                           │  ┌────────────────┐  │
    └──────────┘                           │  │  JSON API      │  │
                                           │  │  /api/*        │  │
                                           │  └────────────────┘  │
                                           │  ┌────────────────┐  │
                                           │  │  SQLite (WAL)  │  │
                                           │  │  + Filesystem  │  │
                                           │  └────────────────┘  │
                                           └──────────────────────┘

Single Docker container running a FastAPI app that serves two roles:

1. MCP Server — mounted at /mcp/ (HTTP transport) and /mcp/sse (SSE transport). AI tools connect here to send and manage mockups.

2. Web Gallery — served at /. Sidebar with project list and chronological feed, main viewer with iframe/image display.

Data Layer

• SQLite in WAL mode — metadata catalog (project, title, description, tags, content type, timestamps)

• Filesystem — mockup files stored in data/{project_slug}/{uuid}.{ext}

• Storage is a bind-mounted data/ directory next to the compose file

Tech Stack

• Python 3.12

• FastAPI + uvicorn

• fastmcp v3.x (standalone)

• SQLite via aiosqlite

• Jinja2 templates + vanilla JS

• Docker + Traefik

Security

There is no built-in authentication. All API endpoints and MCP tools are open to anyone who can reach the server. This is designed for trusted networks (LAN, VPN, Tailscale) or behind a reverse proxy that handles auth. If you deploy this on a public network, add authentication at the proxy layer.

MCP Tools

The server stores all content permanently. AI clients can clean up local files when they're no longer needed, or retrieve content later via get_mockup.

API Routes

Setup

1. Clone and configure

git clone https://github.com/kgNatx/mockups-mpc.git
cd mockups-mpc

2. Deploy

Quick start (pre-built image):

docker compose -f docker-compose.local.yml up -d
# Gallery available at http://localhost:8000

Build from source:

docker compose -f docker-compose.local.yml up -d --build

Production (with Traefik):

cp .env.example .env
# Edit .env with your domain and Traefik network name
docker compose up -d --build

3. Verify

curl http://localhost:8000/health
# {"status":"ok"}

4. Connect Claude Code

claude mcp add-json mockups-gallery '{"type":"http","url":"https://your-domain.com/mcp"}'

Or add to .mcp.json (project-level) or ~/.claude/.mcp.json (global):

{
  "mcpServers": {
    "mockups-gallery": {
      "type": "http",
      "url": "https://your-domain.com/mcp"
    }
  }
}

5. Connect Claude Desktop

Add to your Claude Desktop config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

• Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "mockups-gallery": {
      "type": "sse",
      "url": "https://your-domain.com/mcp/sse"
    }
  }
}

6. Tell your AI to use it

Add instructions to your CLAUDE.md (or equivalent) so your AI uploads mockups via curl instead of passing file content through the model context:

# Mockups

When generating UI mockups, design concepts, or visual prototypes,
write the file locally then upload it to the Mockups MPC gallery via curl:

    curl -s -X POST https://your-domain.com/api/upload \
      -F file=@/path/to/file.html -F project=name -F title=name \
      [-F description=text] [-F "tags=a,b,c"]

To read a mockup's content later, use `get_mockup` to get its
`view_url`, then curl it.

Add to ~/.claude/CLAUDE.md for all projects, or a project's CLAUDE.md for specific ones.

Gallery UI

The gallery auto-seeds a Setup Guide as the first entry on fresh installs. The guide covers all configuration methods with copy-able code blocks.

Layout: Sidebar (project list + chronological feed with title filter + infinite scroll) + main viewer (iframe for HTML, img for images/SVG) + metadata bar + pop-out link.

Theme: Techno Chic Minimalist — Space Grotesk, cyan accents, zinc/neutral dark backgrounds.

Project Structure

app/
├── main.py          # FastAPI app, lifespan, MCP mount, router includes
├── config.py        # Settings (DATA_DIR, DB_PATH, BASE_URL from env)
├── db.py            # SQLite init + CRUD (WAL mode, aiosqlite)
├── models.py        # Pydantic models
├── storage.py       # Slug generation, file write/read/delete, 25MB limit
├── mcp_server.py    # FastMCP instance, tool logic, tool wrappers
├── seed.py          # Auto-seed setup guide on empty DB
├── routes/
│   ├── api.py       # JSON API endpoints
│   └── gallery.py   # Gallery page + raw mockup serving
├── templates/
│   └── gallery.html # Jinja2 gallery template
└── static/
    ├── style.css         # Gallery theme
    └── setup-guide.html  # Self-contained setup guide page

Development

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pytest tests/ -v
uvicorn app.main:app --reload

The test suite covers storage, database, MCP tools, API routes, upload, and gallery.

License

MIT — see LICENSE.