ZPL-er

Overview

ZPL‑er is a Model Context Protocol (MCP) server that translates Zebra Programming Language (ZPL) code into raster images, specifically PNG files. By exposing this conversion as an MCP service, the tool lets AI assistants such as Claude or other MCP‑compatible clients generate, review, and refine shipping label designs without leaving the assistant’s environment. The server bridges the textual nature of ZPL with visual feedback, enabling rapid iteration on label layouts, barcodes, and other printer‑specific elements.

The core problem ZPL‑er addresses is the disconnect between code generation and visual validation. When an AI model writes ZPL, developers traditionally copy the output into a Zebra printer or simulator to see the result. This round‑trip is slow and error‑prone, especially when fine‑tuning positioning or font details. ZPL‑er eliminates that friction by providing an instant PNG preview of any ZPL snippet, allowing developers to compare the rendered label against a target design or to debug misalignments directly within their AI workflow.

Key capabilities of the server include:

• Real‑time rendering: Convert any ZPL string to a high‑resolution PNG on demand.
• Debugging support: Visualise positioning, field boundaries, and barcode placement to spot formatting issues quickly.
• Design iteration: Generate ZPL from design specifications (e.g., Figma exports) and compare successive versions side‑by‑side.
• MCP integration: Operates over standard input/output, making it compatible with a wide range of AI assistants and IDEs that support MCP.

Typical use cases span logistics, retail, and manufacturing. A shipping software developer can ask an AI assistant to create a label for a new product, receive the PNG preview instantly, tweak the code if necessary, and repeat—all within the assistant’s chat or IDE pane. In quality‑control scenarios, a QA engineer can feed test ZPL into the server and verify that barcodes render correctly before deploying to production printers.

Because ZPL‑er is lightweight and written in Go, it can run locally on a developer’s machine or be deployed as part of an internal toolchain. Its single‑command interface and MCP compliance mean it plugs into existing AI workflows with minimal configuration, providing a seamless bridge between code generation and visual verification that accelerates label development cycles.