PDF RAG
Enables intelligent search and question-answering over PDF documents using semantic similarity and keyword search. Supports OCR for scanned PDFs, persistent vector storage with ChromaDB, and maintains source tracking with page numbers.
What is PDF RAG?
PDF RAG is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to enables intelligent search and question-answering over pdf documents using semantic similarity and keyword search. supports ocr for scanned pdfs, persistent vector storage with chromadb, and maintains...
Enables intelligent search and question-answering over PDF documents using semantic similarity and keyword search. Supports OCR for scanned PDFs, persistent vector storage with ChromaDB, and maintains source tracking with page numbers.
This server falls under the Search & Data Extraction category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Enables intelligent search and question-answering over PDF d
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx pdf-rag-mcp-serverConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use PDF RAG
The PDF RAG MCP server provides intelligent search and question-answering over PDF documents using semantic similarity and keyword search, with OCR support for scanned PDFs, persistent vector storage via ChromaDB, and full source tracking including page numbers. It connects to Cursor and other MCP-compatible AI tools through an HTTP-based MCP endpoint, making it easy to query large document libraries, research papers, or technical manuals without manually searching page by page. Source citations with page numbers ensure you can verify every answer the AI provides.
Prerequisites
- Python 3.8 or higher installed
- uv package manager (install via 'curl -sS https://astral.sh/uv/install.sh | bash')
- Tesseract OCR installed on your system (needed for scanned PDF support; e.g. 'brew install tesseract' on macOS)
- Git to clone the repository
- Cursor or another MCP-compatible client that supports HTTP-based MCP endpoints
Clone the repository
Clone the pdf-rag-mcp-server repository to your local machine.
git clone https://github.com/hyson666/pdf-rag-mcp-server.git
cd pdf-rag-mcp-serverInstall Python dependencies
Initialize a uv environment, activate it, and install all backend dependencies including ChromaDB, the PDF processing libraries, and OCR support.
uv init .
uv venv
source .venv/bin/activate
uv pip install -r backend/requirements.txtLaunch the server
Start the full application stack using run.py. This launches the FastAPI backend, MCP server, and WebSocket processing pipeline together.
uv run run.pyUpload PDFs via the web interface
Navigate to http://localhost:8000 in your browser. Upload your PDF documents and wait for the real-time processing indicator to show completion. ChromaDB will persist the vector embeddings across restarts.
Add the MCP server to Cursor
Open Cursor Settings > Cursor Settings > MCP, click 'Add new global MCP server', and paste the configuration pointing to the local HTTP MCP endpoint at port 7800.
{
"mcpServers": {
"pdf-rag": {
"url": "http://localhost:7800/mcp"
}
}
}PDF RAG Examples
Client configuration
Cursor mcp.json configuration to connect to the PDF RAG MCP server's HTTP endpoint for semantic PDF search.
{
"mcpServers": {
"pdf-rag": {
"url": "http://localhost:7800/mcp"
}
}
}Prompts to try
Example prompts for querying your PDF knowledge base once the server is running and documents are processed.
- "Find all mentions of 'data retention policy' in my uploaded PDFs and show the page numbers."
- "What does section 4.2 of the architecture document say about caching?"
- "Search for information about JWT token expiry across all my documents."
- "Which PDF and page number discusses the error code E-404?"
- "Give me a summary of Chapter 3 from the API specification PDF."Troubleshooting PDF RAG
OCR extraction fails for scanned PDFs
Install Tesseract OCR on your system: 'brew install tesseract' on macOS, 'apt-get install tesseract-ocr' on Ubuntu. Also install the pytesseract Python wrapper: 'uv pip install pytesseract'. Restart the server after installation.
ChromaDB errors on restart — vector store seems empty
ChromaDB persists data to a local directory. Check that the backend has write permissions to its working directory. If the database is corrupted, delete the chroma_db folder in the backend directory and re-upload your documents.
Cursor shows the server as inactive after adding the config
Make sure 'uv run run.py' is running in a terminal before enabling the server in Cursor. The MCP toggle in Cursor requires the HTTP endpoint at localhost:7800 to be reachable at the moment you enable it.
Frequently Asked Questions about PDF RAG
What is PDF RAG?
PDF RAG is a Model Context Protocol (MCP) server that enables intelligent search and question-answering over pdf documents using semantic similarity and keyword search. supports ocr for scanned pdfs, persistent vector storage with chromadb, and maintains source tracking with page numbers. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install PDF RAG?
Follow the installation instructions on the PDF RAG GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with PDF RAG?
PDF RAG works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is PDF RAG free to use?
Yes, PDF RAG is open source and available under the MIT License license. You can use it freely in both personal and commercial projects.
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Set Up PDF RAG in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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