QR Code Generation MCP Server

The QR Code Generation MCP Server solves a common need in modern AI workflows: converting arbitrary text—whether plain English, Chinese characters, or any Unicode payload—into a machine‑readable QR code that can be embedded directly into documents, chat interfaces, or printed media. By exposing this capability through the Model Context Protocol (MCP), developers can seamlessly integrate QR code creation into conversational agents, data pipelines, or web services without managing image generation libraries locally.

At its core, the server accepts a simple text payload and produces a base64‑encoded PNG image of the corresponding QR code. The implementation is built on FastMCP, which provides robust support for multiple transport modes (STDIO, HTTP, and SSE). This flexibility allows the server to run in a variety of environments: as a local daemon for desktop AI assistants, within Docker containers for scalable deployment, or directly via the command line for quick testing. The base64 output can be embedded in HTML tags, data URLs, or decoded back into binary images for further processing.

Key features of the server include:

• Unicode‑friendly encoding that handles Chinese characters and other non‑ASCII text without loss.
• Customizable visual style: developers can adjust the box size, border thickness, foreground and background colors to match branding or accessibility requirements.
• Data URL support: an optional flag returns the image as a string, simplifying embedding in web pages or markdown documents.
• Transport versatility: the server can be accessed via simple HTTP POST requests, streamed SSE events for real‑time updates, or STDIO for local integration with tools like Claude Desktop.

Real‑world use cases abound. A chatbot could generate a QR code for a user’s contact information, a ticketing system might embed event codes in printable receipts, and an IoT platform could provide quick configuration links via QR codes displayed on device screens. Because the server is MCP‑compatible, these scenarios can be orchestrated within larger AI pipelines—e.g., a language model generates the QR content, passes it to this server via an MCP tool call, and then returns the resulting image back to the user.

What sets this MCP server apart is its lightweight, container‑friendly design coupled with full color and styling control. Developers can spin it up in minutes, expose it over the network, or run it locally without any heavy dependencies. The base64 output eliminates file‑system concerns and makes the service ideal for stateless, cloud‑native architectures. Overall, the QR Code Generation MCP Server turns a simple image‑generation task into an easily consumable AI tool, enabling richer interactions and smoother integrations across diverse platforms.