# Mockups Gallery [Health: Active]

**Category:** 💻 Developer Tools  
**Repository:** https://github.com/kgNatx/mockups-mpc  
**GitHub Stars:** 2  
**Views:** 0  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/mockups-gallery

## Description
Self-hosted MCP server and gallery for AI-generated mockups.

## Tools
Capabilities this server exposes over MCP:

- **send_mockup** — Send HTML/SVG (raw string) or PNG/JPG (base64) to the gallery. Returns a gallery URL.
- **list_mockups** — List mockups reverse-chronologically, optionally filtered by project.
- **get_mockup** — Get a specific mockup by UUID with view and gallery URLs. Curl the `view_url` to read the file content.
- **update_mockup** — Update metadata (title, description, tags) or replace content.
- **delete_mockup** — Delete a mockup — removes both the DB record and file on disk.
- **tag_mockup** — Add or remove tags on an existing mockup.

## Claude Desktop Quick Installation
Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low):

```json
"mcpServers": {
  "mockups-gallery": {
    "command": "npx",
    "args": ["-y","mockups-gallery"]
  }
}
```

## Documentation & README

# Mockups MPC

[![CI](https://github.com/kgNatx/mockups-mpc/actions/workflows/ci.yml/badge.svg)](https://github.com/kgNatx/mockups-mpc/actions/workflows/ci.yml)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)

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 `curl`s it to the server, keeping file content entirely out of the model context. MCP tools handle lightweight operations only: listing, metadata, tagging, and deletion.

![Mockups MPC — gallery demo](https://raw.githubusercontent.com/kgNatx/mockups-mpc/HEAD/docs/gallery-demo.gif)

## 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)

## 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

| Tool | Description |
|------|-------------|
| `send_mockup` | Send HTML/SVG (raw string) or PNG/JPG (base64) to the gallery. Returns a gallery URL. |
| `list_mockups` | List mockups reverse-chronologically, optionally filtered by project. |
| `get_mockup` | Get a specific mockup by UUID with view and gallery URLs. Curl the `view_url` to read the file content. |
| `update_mockup` | Update metadata (title, description, tags) or replace content. |
| `delete_mockup` | Delete a mockup — removes both the DB record and file on disk. |
| `tag_mockup` | Add or remove tags on an existing mockup. |

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

| Route | Purpose |
|-------|---------|
| `GET /` | Gallery UI |
| `GET /view/{id}` | Raw mockup (HTML rendered, images served with correct MIME type) |
| `GET /api/mockups` | JSON listing with `limit`, `offset`, `project` filter |
| `GET /api/mockups/{id}` | Single mockup metadata |
| `GET /api/projects` | Project list with counts |
| `POST /api/upload` | Upload a mockup file (multipart form: `file`, `project`, `title`, `description?`, `tags?`) |
| `GET /health` | Health check |

## Setup

### 1. Clone and configure

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

### 2. Deploy

**Quick start (pre-built image):**

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

**Build from source:**

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

**Production (with Traefik):**

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

### 3. Verify

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

### 4. Connect Claude Code

```bash
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):

```json
{
  "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`

```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:

```markdown
# 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

```bash
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](https://github.com/kgNatx/mockups-mpc/blob/HEAD/LICENSE).