Typst MCP Server

Overview

The Typst MCP Server bridges the gap between AI assistants and the Typst typesetting system. By exposing a set of practical tools over the Model Context Protocol, it lets language models seamlessly query Typst documentation, convert LaTeX snippets to native Typst syntax, validate code, and even render visual output. This functionality is especially valuable for developers who want to integrate high‑quality typesetting into conversational AI workflows without manually handling the intricacies of Typst.

The server solves a common pain point: while many LLMs excel at writing LaTeX, they struggle with Typst’s newer markup style. Instead of forcing the model to learn a new language, the MCP server provides an automated conversion layer () that leverages Pandoc. It also offers a validation step () so that the model can confirm syntax correctness before presenting code to a user. These two steps together create a reliable pipeline for generating correct, publish‑ready Typst documents directly from conversational prompts.

Beyond conversion and validation, the server exposes tools to navigate the extensive Typst documentation. gives an overview of available chapters, while (and its batch variant) fetches specific content. This enables a model to retrieve context‑specific guidance—such as how to use a particular layout feature—without the user needing to search the documentation manually. The integration of documentation retrieval with conversion and validation makes the server a one‑stop shop for Typst knowledge.

Rendering is another key capability. turns a snippet into a PNG image, allowing multimodal models to preview complex illustrations before rendering them in the final document. This is particularly useful for design‑heavy workflows where visual feedback is critical. By providing a direct image output, the server removes the need for external rendering tools and streamlines the feedback loop.

In practice, developers can embed this MCP server into a Claude or other LLM‑based assistant to build tools such as:

• Automated report generation that accepts LaTeX input and produces a polished Typst PDF.
• Educational assistants that pull relevant documentation chapters on demand and validate student‑written Typst code.
• Design prototypes where the model renders layout examples as images for quick iteration.

Overall, the Typst MCP Server offers a cohesive set of primitives that turn an LLM into a powerful typographic assistant, reducing manual effort and ensuring high‑quality output across text, code, and visual domains.