printagent: order print from inside an AI assistant

An MCP server that lets Claude, ChatGPT, Cursor or any MCP client do what a customer does on a print webshop: find a product → get a price → check the artwork → fix it → approve a proof → place a draft order. All of it happens in one conversation.

"I need 500 business cards for my bakery, here's my logo file." → the assistant picks the product, quotes €54.45, sees the file has no bleed, fixes it, shows a proof with the cut line, spots that "Rotterdam" is misspelled, and drafts the order.

Unofficial prototype built for my HelloPrint application. The catalog and prices are mock data.

Why this problem

At an online print shop, artwork issues are a big, quiet cost. Missing bleed, low-resolution logos, RGB colours, fonts that aren't embedded and text too close to the cut all lead to the same chain: rejected files, support tickets, delays and reprints. Most of these checks are mechanical, so code can do them. The rest (typos, a wrong phone number) needs eyes, and an LLM can provide those.

So the design splits the work:

Related MCP server: @placraftic/mcp-server

AI proofreading in the web checker

Rules can't read. The web checker has a Proofread with AI button that sends the artwork to Claude (vision) and adds what it finds to the same report: typos, wrong-looking contact details, cut-off logos, text too small or low-contrast. On the Hidden typo sample every rule passes and the AI flags "Rotterdma".

To turn it on, copy .env.example to .env and add your ANTHROPIC_API_KEY. Without a key everything else works and the button stays off.

Same engine, three surfaces

1. Web upload (printagent-web): drop a file, get a traffic-light report, a proof with the cut line and a one-click fix. Anyone gets it in five seconds.

2. Customer-service agent: the same report answers "why was my file rejected?".

3. AI assistants over MCP: Claude or ChatGPT orders on the customer's behalf.

Tools

Run it

Windows: double-click install_windows.bat (installs, runs tests and the demo, registers the server in Claude Desktop), then start_web.bat for the web checker.

pip install -e ".[dev]"
python samples/make_samples.py     # 9 test cards, each with a classic mistake
pytest -q                          # 21 tests (AI calls are mocked)
python demo.py samples/03_no_bleed.png   # full flow over real MCP stdio
printagent-web                     # web checker at http://127.0.0.1:8765

Claude Desktop (claude_desktop_config.json):

{ "mcpServers": { "printagent": { "command": "printagent" } } }

(or run python scripts/setup_claude_desktop.py inside the venv to add it automatically.)

Then ask: "Order 500 business cards with this file: ~/Desktop/card.png".

For accurate colour, point PRINTAGENT_CMYK_ICC at a press profile (e.g. Coated FOGRA39). Without one, the server falls back to a generic conversion and says so.

Test set

What I'd do next with real access

1. Plug in the real catalog and pricing API instead of catalog.py (the tools don't change).

2. Measure it: run preflight over a sample of historical uploads and compare with files that ended up in support tickets or reprints. That gives precision and recall per check, plus the euros saved.

3. Use the same engine in three places: the upload step on the website (instant feedback), the customer-service agent (answers "why was my file rejected?"), and the MCP server for agentic commerce.

4. Vector-preserving PDF fixes (add bleed without rasterising), spot colours, and checks for minimum line weight and text size.

5. Checkout handoff: draft order → payment link, so the assistant never touches card data.

Known limitations

• Safe-zone detection uses edge density, so a busy photo running off the edge can trigger a (harmless) warning. A small vision model or text detection would make it sharper.

• Only page 1 is visually checked; fonts and page count are checked on pages 1–2.

• Without a press ICC profile (PRINTAGENT_CMYK_ICC), CMYK conversion uses a generic formula and colours shift more.

• Mock catalog and prices.