pdf-rag-mcp

> MCP server for RAG over messy PDFs — extract, chunk, embed, and search scanned, multi-column, and table-heavy documents.

[](https://www.python.org/downloads/) [](LICENSE) [](https://modelcontextprotocol.io)

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<p align="center"> <br/> <em>All 6 tools running in the MCP Inspector</em> </p>

RAG (Retrieval-Augmented Generation) is a technique that makes AI assistants smarter by giving them access to your own documents. Instead of relying only on training data, the AI first *retrieves* relevant chunks from your files, then uses them as context to generate accurate, grounded answers.

Traditional AI:  User Question → LLM → Answer (may hallucinate)
RAG:             User Question → Search Your Docs → LLM + Context → Accurate Answer

This MCP server is the "Search Your Docs" part — it ingests PDFs, breaks them into searchable chunks, and lets any MCP-compatible AI assistant find the right information instantly.

Most PDF tools choke on real-world documents — scanned pages, multi-column layouts, embedded tables. This MCP server handles them all:

• Scanned PDFs — Automatic OCR via Tesseract when text extraction fails
• Multi-column layouts — Layout-preserving block sorting with PyMuPDF
• Tables — Detected and extracted as clean markdown via pdfplumber
• Semantic search — Find information by meaning, not just keywords
• 100% local — Embeddings run on your machine. No data leaves your system.

<p align="center"> <br/> <em>Ingesting a PDF — extracts text, chunks it, generates e