mcp-pandoc

The mcp-pandoc server turns the powerful document conversion engine Pandoc into a ready‑to‑use Model Context Protocol endpoint. It addresses the common pain point of moving content between Markdown, HTML, LaTeX, DOCX, PDF and many other formats without sacrificing structure or styling. Developers can now ask an AI assistant to transform a draft, a report, or a website snippet into any target format and receive the fully rendered output directly in their conversation.

At its core, the server exposes a single tool—. This tool accepts either raw text or an existing file path, along with the source and target formats. The server internally delegates to Pandoc’s conversion pipeline, ensuring that tables, code blocks, math expressions and custom styling survive the transformation. Because the tool is wrapped in MCP, it can be invoked from any AI client that supports the protocol, making document manipulation a first‑class feature of conversational workflows.

Key capabilities include:

• Bidirectional conversion: Convert Markdown to PDF, DOCX to LaTeX, HTML to reStructuredText, and more.
• File‑based processing: Work with large documents without sending the entire text over the network.
• Format‑preserving transformations: Maintain complex layouts, footnotes, and embedded media during conversion.
• Extensible format support: Add new Pandoc formats or custom templates with minimal effort.

Real‑world use cases abound. A technical writer can draft in Markdown, then ask the assistant to produce a polished PDF or Word document for publication. A data scientist can convert Jupyter notebook outputs into HTML reports, while a web developer might transform Markdown documentation into a static site format. In educational settings, teachers can generate LaTeX handouts from plain text lessons, and in compliance workflows, legal teams can convert contracts into secure PDF or DOCX formats for archival.

Integrating mcp-pandoc into AI pipelines is straightforward: once the server is running, any MCP‑enabled assistant can call as part of a larger chain—extracting text from a PDF, reformatting it, and then sending the result back to the user. The server’s lightweight design means it can run locally or be deployed in the cloud, providing a seamless bridge between natural language commands and robust document conversion.