"High-Quality PDF Extraction to Text with Tokenization and Accurate Processing of Complex Layouts" matching MCP servers:

Converts PDF files to text for use with MCP-compatible applications like Cursor IDE.

Enables structured information extraction from academic PDFs using LLMs, integrating with Claude Desktop for natural language querying and batch processing.

Enables AI agents to screen and filter stock universe, compute custom quality scores, inspect score history, manage scoring systems, and generate shareable screen links, acting as the signed-in user.

MCP server for Aspose PDF Cloud, enabling PDF storage, conversion, and extraction operations (merge, split, text/table/image extraction) via CLI and MCP.

Enables AI-powered extraction and analysis of PDF documents with 40+ specialized tools for text, tables, images, layout analysis, security assessment, and document intelligence. Supports both text-based and scanned PDFs with OCR capabilities.

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.

Enables AI agents to read and analyze PDF documents for natural language Q\&A. Supports multiple LLM providers including Google Gemini, Anthropic Claude, and OpenAI.

Enables assistants to search a Zotero library, retrieve metadata and PDF text, and view PDF pages as rendered images so figures and layouts are visible.

A Model Context Protocol server that enables the extraction of text, metadata, and embedded images from PDF files. It provides tools for searching text with context, reading specific pages, and counting total pages within a document.

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.

Enables querying normalized real-world asset data through MCP tools, including listing assets, retrieving asset details, comparing yields against SOFR, and checking eligibility by jurisdiction.

Provides random access to PDF contents with selective page extraction, text search, outline navigation, image extraction, and page rendering capabilities. Reduces token usage by allowing targeted content extraction instead of processing entire documents.

Read and filter Excel, CSV, ODS, SQLite, Parquet and MTSV files — or a folder — as plain text

Algorithmic text and NLP analysis server providing sentiment, readability, keyword extraction, language detection, stats, and more via REST and MCP.

Enables processing and analysis of large PDF files through text extraction, search functionality, and intelligent chunking strategies. Provides comprehensive PDF operations including metadata retrieval, page-range text extraction, and content search with contextual results.

Enables MCP clients to inspect PDF structure and extract object-level data such as characters, words, tables, images, fonts, and metadata, with JSON/CSV export and image retrieval.

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

Provides deterministic, schema-driven structured data extraction from PDF documents via regex/heuristic matching, enabling repeatable, inspectable, and cost-free field extraction without LLM API calls.

An MCP server that enables AI assistants to read, search, and analyze PDF files from local paths or URLs. It provides tools for extracting specific page ranges, searching for terms, and retrieving document metadata.

Enables AI agents to process and inspect PDFs by detecting document type, extracting text, and generating Markdown with layout information.