LaTeX to MathML MCP Server

The LaTeX to MathML MCP Server bridges the gap between LaTeX‑centric mathematical notation and the web‑friendly MathML format. In many AI workflows, developers need to render or process mathematical expressions within browsers, assistive tools, or data pipelines that require a structured markup language. This server solves that problem by providing an on‑demand, lightweight conversion service that can be queried directly from any MCP client.

At its core, the server exposes two complementary interfaces: a tool named and a resource URI pattern (). The tool accepts a raw LaTeX string and returns the corresponding MathML markup in a standardized MCP response. The resource interface allows clients to treat each LaTeX expression as a first‑class URI, enabling caching, linking, and incremental updates without maintaining state on the client side. Both interfaces are built using the official MCP SDK, ensuring that authentication, transport negotiation, and error handling follow protocol best practices.

Key capabilities include:

• Fast, deterministic conversion powered by MathJax‑node, which guarantees that the same LaTeX input always yields identical MathML output.
• URL‑encoded resource access, allowing developers to embed mathematical expressions directly in URLs and retrieve them on demand.
• Lightweight deployment: a single Node.js script that listens over stdio, making it trivial to run as part of a larger AI assistant stack or within containerized environments.

Typical use cases span from educational platforms that need to render student‑submitted equations in a web interface, to research assistants that parse LaTeX papers and convert them into accessible MathML for screen readers. In a conversational AI setting, an assistant can generate LaTeX on the fly and immediately hand it to this server for MathML output, which can then be injected into a rich‑text editor or displayed in a mobile app.

Because the server follows the standard MCP protocol, integrating it into existing AI workflows is straightforward. Clients can declare the server in their configuration, invoke the tool with a simple JSON payload, or fetch MathML via the resource URI. This seamless interaction reduces boilerplate and lets developers focus on higher‑level logic rather than parsing or rendering concerns.