Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@PDF RAG MCP Server search for quantum computing breakthroughs in my documents"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for RAG over messy PDFs — extract, chunk, embed, and search scanned, multi-column, and table-heavy documents.
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 AnswerThis 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.
Related MCP server: PDF RAG MCP Server
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.
Tool: pdf_ingest
Input: { "file_path": "/home/user/reports/ai-healthcare-2026.pdf" }{
"doc_id": "a1b2c3d4e5f6",
"filename": "ai-healthcare-2026.pdf",
"total_pages": 4,
"total_chunks": 12,
"scanned_pages": [],
"status": "ingested"
}Tool: pdf_search
Input: { "query": "drug discovery timelines", "limit": 3 }{
"query": "drug discovery timelines",
"total_results": 3,
"results": [
{
"text": "Drug discovery timelines shortened by 30% using generative AI models...",
"score": 0.8742,
"page_num": 1,
"source_filename": "ai-healthcare-2026.pdf"
},
{
"text": "The healthcare AI market is experiencing rapid growth... Drug Discovery 8.7B...",
"score": 0.6521,
"page_num": 3,
"source_filename": "ai-healthcare-2026.pdf"
}
]
}Tool: pdf_extract_tables
Input: { "file_path": "/home/user/reports/ai-healthcare-2026.pdf", "page_num": 3 }{
"page_num": 3,
"tables_found": 1,
"markdown": "| Application | Market 2025 | Market 2030 |\n|---|---|---|\n| Diagnostic Imaging | $12.4B | $45.2B |\n| Drug Discovery | $8.7B | $32.1B |"
}Tool: pdf_get_page
Input: { "doc_id": "a1b2c3d4e5f6", "page_num": 1 }{
"doc_id": "a1b2c3d4e5f6",
"page_num": 1,
"text": "Artificial Intelligence in Healthcare\nA Comprehensive Report - 2026\n\nExecutive Summary\nArtificial intelligence is transforming healthcare delivery..."
}Tool: pdf_list_documents{
"total_documents": 2,
"documents": [
{ "doc_id": "a1b2c3d4e5f6", "source_filename": "ai-healthcare-2026.pdf", "total_chunks": 12, "total_pages": 4 },
{ "doc_id": "f6e5d4c3b2a1", "source_filename": "quarterly-report.pdf", "total_chunks": 45, "total_pages": 18 }
]
}Tool: pdf_delete
Input: { "doc_id": "f6e5d4c3b2a1" }
→ { "doc_id": "f6e5d4c3b2a1", "chunks_deleted": 45, "status": "deleted" }Tool | Description |
| Ingest a PDF: extract text (with OCR fallback), chunk, embed, and store |
| Semantic search across all ingested PDFs with similarity scores |
| Get full extracted text for a specific page |
| List all ingested documents with metadata |
| Remove a document and its embeddings from the store |
| Extract tables from a page as markdown |
Python 3.12+
Tesseract OCR (for scanned PDF support)
# Ubuntu/Debian
sudo apt install tesseract-ocr
# macOS
brew install tesseractgit clone https://github.com/MBaranekTech/pdf-rag-mcp.git
cd pdf-rag-mcp
uv venv .venv && source .venv/bin/activate
uv pip install -e .pip install pdf-rag-mcpAdd to ~/.config/Claude/claude_desktop_config.json (Linux) or ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):
{
"mcpServers": {
"pdf-rag": {
"command": "/path/to/pdf-rag-mcp/.venv/bin/pdf-rag-mcp"
}
}
}claude mcp add pdf-rag -- /path/to/pdf-rag-mcp/.venv/bin/pdf-rag-mcpAdd to .cursor/mcp.json or VS Code MCP settings:
{
"mcpServers": {
"pdf-rag": {
"command": "/path/to/pdf-rag-mcp/.venv/bin/pdf-rag-mcp"
}
}
}docker build -t pdf-rag-mcp .
docker run -v /path/to/pdfs:/pdfs pdf-rag-mcpPDF File
│
▼
┌─────────────────────────────────────────┐
│ pdf_extractor.py │
│ ┌───────────┐ ┌──────────────────┐ │
│ │ PyMuPDF │──▶│ Text extraction │ │
│ └───────────┘ │ (layout-aware) │ │
│ ┌───────────┐ ├──────────────────┤ │
│ │ Tesseract │──▶│ OCR fallback │ │
│ └───────────┘ │ (scanned pages) │ │
│ ┌───────────┐ ├──────────────────┤ │
│ │pdfplumber │──▶│ Table detection │ │
│ └───────────┘ └──────────────────┘ │
└──────────────────┬──────────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ chunker.py │
│ Split into ~500-word overlapping │
│ chunks with page number metadata │
└──────────────────┬──────────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ vector_store.py │
│ ┌────────────────────┐ ┌───────────┐ │
│ │ sentence-transformers│ │ ChromaDB │ │
│ │ (all-MiniLM-L6-v2) │─▶│ (cosine) │ │
│ └────────────────────┘ └───────────┘ │
└──────────────────┬──────────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ server.py (FastMCP) │
│ 6 tools exposed via MCP protocol │
└─────────────────────────────────────────┘Ingest — PyMuPDF extracts text blocks sorted by position. Pages with < 50 characters of text are automatically OCR'd via Tesseract.
Chunk — Text is split into ~500-word overlapping chunks (50-word overlap), preserving page number metadata.
Embed — Chunks are embedded using all-MiniLM-L6-v2 (~80MB, runs locally, no API keys).
Store — Embeddings and metadata are persisted in ChromaDB at ~/.pdf-rag-mcp/chroma_db/.
Search — Queries are embedded and matched against stored chunks using cosine similarity.
# Setup
uv venv .venv && source .venv/bin/activate
uv pip install -e ".[dev]"
# Run tests
pytest tests/ -v
# Test with MCP Inspector (requires Node.js)
fastmcp dev inspector src/pdf_rag_mcp/server.py:mcp --with-editable .
# Opens browser UI at http://localhost:6274Component | Technology |
MCP Framework | |
PDF Extraction | |
Table Extraction | |
OCR | Tesseract via pytesseract |
Embeddings | sentence-transformers (all-MiniLM-L6-v2) |
Vector Store |
All processing happens locally:
Embedding model runs on your machine
PDF content is never sent to external APIs
Data stored at ~/.pdf-rag-mcp/chroma_db/
Remove an ingested PDF and all its embeddings from the store.
Inputs: doc_id (required). Returns: object
Extract tables from a specific page of a PDF and return them as markdown.
Inputs: page_num (required), file_path (required). Returns: object
Get the full extracted text for a specific page of an ingested PDF.
Inputs: doc_id (required), page_num (required). Returns: object
Ingest a PDF file: extract text (with OCR fallback for scanned pages), split into chunks, generate embeddings, and store for search.
Inputs: file_path (required). Returns: object
List all ingested PDF documents with their metadata.
Returns: List of documents with doc_id, filename, page count, chunk count, and ingest date.
Inputs: no named parameters. Returns: object
Semantic search across all ingested PDFs. Returns the most relevant text chunks ranked by similarity.
Inputs: query (required), limit, doc_id. Returns: object
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 6 tools
Each tool targets a distinct operation: ingest, search, page retrieval, table extraction, listing, and deletion. There is no meaningful overlap between tool purposes, so an agent can clearly select the right tool.
All tools follow a consistent pdf_ prefix with verb-first naming, such as pdf_ingest, pdf_search, and pdf_get_page. The pattern is predictable and uniformly snake_case throughout.
Six tools is well-scoped for a PDF RAG server, covering ingestion, retrieval, and document management without unnecessary redundancy. Each tool earns its place in the set.
The tool surface covers the full PDF RAG lifecycle: ingest, search, retrieve page context, extract tables, list documents, and delete. There are no obvious dead ends or missing core operations for the stated purpose.
High-fidelity PDF to structured Markdown conversion and document field extraction.
Extract text, tables and metadata from every PDF linked in a dataset, CSV or Google Sheet.
PDF URLs to per-page text, tables as rows, Markdown, metadata and OCR for scanned pages.
Extract tables, text and formulas from PDFs, including scanned pages and broken text layers.