An MCP server for reading PDFs
Configuration
View docs{
"mcpServers": {
"averagejoeslab-pdf-reader-mcp": {
"url": "http://localhost:8000/sse"
}
}
}You have a PDF reading and analysis MCP server designed to extract, index, and reason about scholarly PDFs. It provides full-text extraction, metadata enrichment, and academic structure awareness so you can process PDFs with natural language prompts and targeted analyses, making it easier to build tooling that understands academic documents.
You connect to the PDF reader MCP server from your MCP client and run natural language requests to load PDFs, extract content, and perform academic analyses. You can load a PDF, retrieve its metadata, extract images and tables, render pages as high-resolution images, and run specialized academic prompts to summarize or analyze methodologies. You can also ask the server to detect sections like Abstract, Introduction, and Methods to quickly navigate long papers.
Summarize the PDF in a technical style focusing on methodology to capture how the study was designed and executed.
Analyze the structure of the PDF to understand its organization and how arguments are built across sections.
Extract citations and build a parsed references list to trace sources and credibility.
Load and cache a PDF for processing and reuse in subsequent requests.
Retrieve document metadata and general information from the PDF.
Extract embedded images from the PDF along with their metadata.
Render a specific PDF page as a high-resolution image.
Extract the full text content from the PDF while preserving reading order.
Detect and extract table data from the document.
Extract comments, highlights, and other annotations.
Extract text with proper reading order and preservation of mathematical formulas.
Identify academic sections such as Abstract, Introduction, Methods, and Results.
Extract only the abstract section from the document.
Provide key sections optimized for agent understanding, such as Abstract, Methods, and Conclusions.
Parse in-text citations and reference lists for easy tracing of sources.
Break content into semantic chunks suitable for agent processing.
Perform a comprehensive analysis of the document’s structure and organization.