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)

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.

Versions

A mockup ("design") can hold more than one version — every revision of the same screen, kept in one place instead of scattering separate mockups.

• Add a version to an existing design: upload with -F parent=<id> (a design or alias id). It's added as the next version; earlier versions and their files are kept.
• Auto-fold: upload without parent and the server still checks for a match. If exactly one design in the same project has the same base title — the title with trailing markers like v2, draft 3, rev 2, r5, a bare trailing number, or trailing parentheticals such as (rail fixed) stripped — the upload becomes a new version of it automatically. Variant markers (option B, variant C, a standalone trailing letter) are never stripped, so "Hero — option B" and "Hero — option C" stay separate designs. Zero or several matches also create a new design. Pass -F fold=false to always create a new design regardless of title. An auto-folded upload's response carries folded: true and a note saying how to split it back out if the match was wrong.
• Direct link to one version: /view/{id}/v/{n} serves that exact version. /view/{id} serves the latest version when {id} is a design id, and the pinned version when {id} is an alias id (see below).
• Folding keeps old links working. When a mockup is folded into another design, its id becomes an alias pinned to the version it turned into, and every read (/view/…, get_mockup, etc.) resolves it transparently. A version link minted before the fold (/view/{old id}/v/1) keeps working too.
• Splitting and deleting a version do remove links. A split-out version moves to its new design as v1, so its old /view/{design}/v/{n} link returns 404 (the new design reuses the version's alias id when it has one, so that id keeps working). Deleting a version removes it and any alias pinned to it.
• Undo a fold: split_version(id, version) pulls one version back out into its own standalone design, reusing its old alias id if it has one.

Merging existing duplicates (opt-in, one-time)

If you were already uploading revisions as separate mockups before this feature existed, python -m app.fold finds and merges them:

python -m app.fold --dry-run     # print the proposed groups, change nothing
python -m app.fold --apply       # perform them
python -m app.fold --dry-run --data-dir /path/to/copy   # run against another data directory

--data-dir points the command at a different data directory (the one holding mockups.db) instead of the configured one — useful for trying the fold on a scratch copy of your data before running it for real.

It groups designs that share a project and a base title, oldest first, and folds each group into one design (the oldest survives; the rest become versions, with their old ids kept as aliases). This never runs automatically — it's a command you run by hand, and only after reviewing the dry-run output, since merging is not reversible in bulk (each design can still be split back out individually with split_version).

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 revise a mockup you already uploaded, add `-F parent=<id>` (the id
from the earlier upload response): the gallery keeps it as a new version
of the same design. Use `-F fold=false` only for a deliberate separate variant.

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. The in-app Setup Guide carries the same snippet; an existing install gets the updated guide as a new version of its Setup Guide on the first start after upgrading.

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.