"Extracting Orders from PDF Files" matching MCP servers:

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

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

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

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.

On-premises knowledge retrieval over the Markdown, text, PDF, Word and Excel files an organization keeps, with the rules you set enforced before anything is retrieved. Agents connect over MCP, get cited passages, and read from it and never write to it.

Enables semantic search across personal PDF paper collections with page-level citations, allowing users to query their library from any MCP-capable client.

Enables AI agents to convert files, render web pages to markdown/PDF/screenshots, search the live web, extract structured data, ingest RAG-ready chunks, and monitor pages for changes through a single API key.

Provides cross-session memory for Claude Code by extracting structured handoffs at session end and retrieving relevant past context at session start.

Four utilities in one server: PDF text, ruled tables and OCR from a URL as pages, documents or RAG chunks; YouTube thumbnail URLs and files with per-size availability; sports scores, schedules and standings across 33 leagues; and ATP/WTA tennis with per-set scores and draws.

Enables searching and retrieving documents from a local folder to ground LLM answers in your files.

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.

MCP server wrapping Docling to give text-only LLMs document vision via tools for converting PDFs, images, and Office files to Markdown, extracting tables, OCR, and RAG chunking.

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 local-first document retrieval MCP server that enables AI coding tools like Codex to search private local documents via semantic search and keyword boost, supporting ingestion of PDF, DOCX, TXT, Markdown, and HTML files.

Enables natural-language search over locally indexed files such as markdown, text, images, videos, and PDFs, and retrieves indexed text or media metadata by path. It lets Cursor query a local embedding index built with Gemini and SQLite.

Search your own files from Claude & any MCP client: PDF, Office and more. Exact full-text search with page numbers, 100% local. Needs the RootMind app.

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

Enables AI agents and MCP-capable platforms to convert documents such as docx, pptx, xlsx, pdf, odt, rtf, epub, and csv into clean GitHub-Flavored Markdown through a single tool call. Runs fully locally with no API key, no network access, and no OCR, returning structured errors for scanned, encrypted, or unsupported files.

Enables AI agents to ingest documentation from websites or local files and perform semantic search over the indexed content using pgvector-backed embeddings.

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

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

Connects Claude to Open WebUI, enabling chat management, RAG knowledge bases, files, functions, and prompts directly from Claude.

Exposes semantic search over a folder of indexed PDF guides as a tool that Claude Code or any MCP client can call. Returns the closest matching passages with relevance scores, source file and page citations, so the client can answer questions about the guides while knowing when nothing relevant was found.

Local MCP server that lets agents read PDF, DOCX, PPT, Excel, HTML, EPUB, email and 40+ other formats as Markdown. Read PDF, Office, HTML, EPUB, email and 40+ formats as Markdown; outline, search and diff documents inside allowlisted folders. Writing documents from Markdown and editing DOCX, PPTX, HTML and Markdown files in place are opt-in. All file access is confined to allowlisted folders.

MCP server for Google's Gemini File Search (RAG). Manage file search stores, upload documents, and query with RAG through 12 tools.

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 forkable RAG project with an MCP server for VS Code/Copilot, enabling indexing and querying of knowledge from URLs, PDFs, or TeX files.

A server implementation for interacting with Wikidata API using the Model Context Protocol, providing tools for searching identifiers, extracting metadata, and executing SPARQL queries.

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.