"PDF" matching MCP servers:

Rust-powered PDF toolkit over MCP: create, read, and analyze PDFs; extract text and entities for RAG; convert to Markdown; split/merge/rotate/reorder pages; manage form fields and annotations; encrypt documents. Runs locally via uvx oxidize-mcp.

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.

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

Enables agents to search local component-manual corpora via hybrid semantic, full-text, and substring retrieval with table recognition and OCR fallback for scanned pages. Provides tools for literal/regex grepping, reading single or multiple pages, navigating bookmarks and outlines, listing categories and indexed files, and checking index and dependency status.

Enables a persistent local library for mathematical PDFs with cached LaTeX extraction, full-text search in English and Greek, and page/image retrieval over MCP.

A local RAG MCP server for PDF development experience, enabling document ingestion, semantic search, and Q\&A with source citations using TF-IDF and cosine similarity.

A Model Context Protocol (MCP) based server that efficiently manages PDF files, allowing AI coding tools like Cursor to read, summarize, and extract information from PDF datasheets to assist embedded development work.

Enables AI assistants to query PDF documents by ingesting them into a vector database and generating answers grounded in the actual documents.

Enables agents to extract clean structured JSON tables from any PDF with per-cell citations to the source page.

Enables local LLMs to match lesson summaries or syllabus topics against PDF textbooks and manuals, returning the exact page ranges relevant to each topic along with brief explanations of why they matter. Runs entirely offline via Ollama embeddings and reasoning plus Tesseract OCR for scanned or image-only pages, so multi-hundred-page documents can be searched privately without API keys or cloud credits.

Enables AI-driven PDF document processing including PDF to Markdown conversion, intelligent text and table extraction, image extraction, format conversion between PDF/Word/Markdown, batch processing, and fuzzy search - optimized for LLM context and RAG workflows.

Enables AI-powered querying of PDF documents using hybrid retrieval (BM25 + vector search) and retrieval-augmented generation, returning structured answers with source citations and confidence scores.

A Model Context Protocol (MCP) server that provides powerful RAG (Retrieval-Augmented Generation) capabilities for PDF documents. This server uses ChromaDB for vector storage, sentence-transformers for embeddings, and semantic chunking for intelligent text segmentation.

Enables AI agents to download, index, and semantically search PDF research papers using 8 MCP tools.

MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.

Enables intelligent ingestion and querying of PDF, Markdown, and text files using hybrid search that combines keyword matching and semantic embeddings with citations.

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

This MCP server enables AI agents to view PDFs as accessible HTML with bounding-box citations, and provides tools for layout-aware parsing, schema extraction, cross-document Q&A, and PDF rendering.

A Model Context Protocol server that enables intelligent document search and retrieval from PDF collections, providing semantic search capabilities powered by OpenAI embeddings and ChromaDB vector storage.

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.