PyMuPDF Utilities for LLM/RAG
PyMuPDF Utilities for LLM/RAG. That is what the pymupdf4llm mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
PyMuPDF4LLM is a lightweight extension for PyMuPDF that converts documents into structured Markdown, JSON, and plain text optimised for RAG pipelines, vector embeddings, and LLM ingestion. It handles multi-column layouts, tables, images, headers, and scanned pages with automatic OCR — all powered by the MuPDF C engine.
The server publishes 1 tool. What each one is for:
Configuration — The default behaviour requires no configuration — just install Tesseract and it works:The server ships on PyPI as pymupdf4llm, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Pymupdf4llm's toolset — Configuration — is a fair guide to whether it matches your workflow. It is maintained by Artifex; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Pymupdf4llm's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| Configuration | The default behaviour requires no configuration — just install Tesseract and it works: |
{
"mcpServers": {
"pymupdf4llm": {
"command": "uvx",
"args": ["pymupdf4llm"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.