"A search for information related to PDF files" matching MCP servers:

MCP server for full-text search across PDF document collections with offline indexing, ranked results, snippets, and page rendering.

Converts vector PDF circuit schematics into evidence-preserving SchematicIR JSON with tools to inspect, convert, and validate schematic PDFs.

Generates test files locally (images, PDFs, CSVs, corrupted files, etc.) from natural language prompts, with configurable sizes and safety limits.

A Model Context Protocol server that enables AI agents to read, search, and extract content from PDF files.

A lightweight MCP server for basic file operations, enabling reading, writing, and listing files securely via the Model Context Protocol.

A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.

Turn markdown into designed PDFs with cover page, table of contents, and code blocks that hold across pages. One command from Claude Desktop, Claude Code, Cursor, Cline, Zed, or any MCP-capable client.

A PDF MCP server for extracting text, rendered pages, tables, links, annotations, outlines, metadata, and text statistics from PDF files.

An MCP server for reading, rendering, and searching PDF files, specifically optimized for LLMs to extract text, tables, and technical diagrams. It enables metadata retrieval, multi-format text extraction, and page-to-image rendering using PyMuPDF.

Converts Markdown files to Word (.docx) and PDF documents. Supports .dotx templates for branded styling and batch conversion.

Memory-efficient MCP server for reading and splitting large PDF files page by page using PyMuPDF.

Provides PDF reading, text search, navigation, and form filling capabilities through MCP, with cross-platform PDF viewer integration for seamless document interaction.

An MCP server for high-quality bidirectional PDF ↔ Markdown conversion with full Unicode/CJK character support, enabling users to convert files via natural language commands.

A server designed for processing PDF documents, enabling text extraction, table data retrieval, and metadata collection from local files. It allows users to scan directories for PDFs and read specific pages, specifically optimized for thesis literature analysis.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

An MCP server that extracts energy-usage charts from utility-bill PDFs, returning structured time-series data and annotated images.

A Model Context Protocol (MCP) server that provides comprehensive PDF redaction capabilities, including text and image redaction, search, and session-based in-memory operations using FastMCP and pymupdf.

MCP server to batch download open-access PDFs from PubMed Central (PMC), supporting PMCID/PMID lookup, parallel downloads, and download reports.

Enables AI assistants to natively read, edit, and redact PDF documents in place through MCP tools, eliminating manual script execution and layout issues.

Enables RAG over messy PDFs — extract, chunk, embed, and search scanned, multi-column, and table-heavy documents.