"Understanding and Building Probability Density Functions (PDF) Models" matching MCP servers:

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 agents to look up facts about notable machine-learning models — organization, release date, parameter count, training-compute (FLOP) estimate, hardware and accessibility — by name, and to search or aggregate them by organization, domain, year, or compute threshold. Supports filtering the dataset newest-first and producing per-year training-compute trends, keylessly and without authentication.

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.

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.

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.

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.

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.

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.

An MCP server that enables users to read, search, and analyze PDF documents. It provides tools for extracting text, viewing metadata, searching content with context, and generating word statistics.

55 Concordance-tested financial models with published specs; responses cite assumptions and sources.

Enables AI agents to parse and extract text, metadata, and statistics from PDF documents via URL, with x402 micropayments on Base Mainnet.

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

MCP server that gives your Claude, Cline, or Cursor session the ability to extract text, tables, and metadata from any PDF URL — including scanned PDFs via OCR.

Extracts text and tables from PDFs for AI agents via MCP, enabling structured data retrieval from invoices, reports, and statements.

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.

FastMCP server for PDF text extraction and metadata retrieval, providing structured JSON output for pages, metadata, and specific page ranges.

Classifies PDFs (text-based vs scanned vs image vs mixed), extracts text, and converts to clean Markdown over Streamable HTTP MCP. Supports remote PDFs via URL or base64, with optional page restriction and OCR fallback detection.

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.

An MCP (Model Context Protocol) server specialized in deciphering PDF internal structures.

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

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

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

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

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.

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

Give AI agents cited funding, money-market, FX and capital-market evidence through 16 public MCP tools. Keep source dates, freshness checks and coverage gaps attached; China macro coverage is metadata-only. No API key is required for the public surface. Research context only; no trade execution.

Enables parsing PDF files from local paths or URLs into structured JSON or Markdown format using NetMind's AI-powered PDF extraction service.

Enables LLMs to read and extract content from PDF files with high-fidelity LaTeX recognition and layout awareness using a Python-based extraction engine. It includes a robust Node.js fallback and supports page range filtering for efficient processing of large documents.