PDF RAG Server
PDF RAG server for cursor.
What is PDF RAG Server?
PDF RAG Server is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to pdf rag server for cursor.
PDF RAG server for cursor.
This server falls under the Search & Data Extraction category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- PDF RAG server for cursor.
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx pdf-ragConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use PDF RAG Server
The PDF RAG MCP server is a full-stack document knowledge base system that lets you upload PDFs, process and vectorize their content, and query them with semantic search through the Model Context Protocol. It pairs a FastAPI backend with a React frontend and a WebSocket-based real-time processing pipeline, and exposes an MCP endpoint that Cursor and other AI tools can connect to for intelligent document question-answering. It is particularly useful for developers who need to query technical manuals, research papers, or large PDF archives directly from their AI coding assistant.
Prerequisites
- Python 3.8 or higher installed
- uv package manager (install via 'curl -sS https://astral.sh/uv/install.sh | bash')
- Git to clone the repository
- Node.js and npm (only needed if rebuilding the React frontend)
- 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 with uv
Initialize a uv environment and install the backend dependencies from the requirements file.
uv init .
uv venv
source .venv/bin/activate
uv pip install -r backend/requirements.txtStart the application
Use the provided run.py script to launch both the FastAPI backend and the MCP server together. The web interface is available at http://localhost:8000 and the MCP endpoint at http://localhost:7800/mcp.
uv run run.pyUpload and process PDFs
Open the web interface at http://localhost:8000, use the upload button to add your PDF documents, and wait for the processing status (shown in real time via WebSocket) to complete before querying.
Configure Cursor to use the MCP server
Open Cursor Settings > Cursor Settings > MCP and add a new global MCP server pointing to the local HTTP endpoint. Enable the toggle once added.
{
"mcpServers": {
"pdf-rag": {
"url": "http://localhost:7800/mcp"
}
}
}PDF RAG Server Examples
Client configuration
Cursor mcp.json configuration to connect to the locally running PDF RAG MCP server via its HTTP endpoint.
{
"mcpServers": {
"pdf-rag": {
"url": "http://localhost:7800/mcp"
}
}
}Prompts to try
Example prompts to use in Cursor or Claude once PDF documents have been uploaded and processed.
- "Search my uploaded PDFs for information about transformer attention mechanisms."
- "What does the PDF say about the installation requirements on page 3?"
- "Find all sections in my documents that mention API authentication."
- "Summarize the key findings from the research paper I uploaded."
- "Which uploaded document covers database indexing strategies?"Troubleshooting PDF RAG Server
The MCP endpoint at http://localhost:7800/mcp is unreachable
Ensure the server is running ('uv run run.py') and has fully started before connecting from Cursor. Check terminal output for any port conflict errors. If port 7800 is in use, check the run.py configuration for an alternative port setting.
PDF processing hangs or shows no progress
Large or heavily scanned PDFs take longer to process. Check the terminal running run.py for error output. If OCR is failing, ensure the required OCR libraries (typically pytesseract and tesseract-ocr) are installed on your system.
Search returns irrelevant results after uploading
Wait for the processing status in the web UI to show 'Completed' before querying. If a document shows an error state, try re-uploading it. Semantic search works best after the vectorization step finishes fully.
Frequently Asked Questions about PDF RAG Server
What is PDF RAG Server?
PDF RAG Server is a Model Context Protocol (MCP) server that pdf rag server for cursor. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install PDF RAG Server?
Follow the installation instructions on the PDF RAG Server GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with PDF RAG Server?
PDF RAG Server works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is PDF RAG Server free to use?
Yes, PDF RAG Server is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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Set Up PDF RAG Server 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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