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

A minimal MCP server that exposes Gemini's web and multimodal understanding (video, audio, document, image) through five dedicated tools.

Enables searching, comparing, and inspecting AI models by pricing, context window, and capabilities via the models.dev catalog.

Provides real-time access to Japanese building standards laws and regulations from e-Gov API, helping prevent AI hallucinations by validating citations against original legal texts.

An MCP server that analyzes local or remote GitHub repositories, providing intelligent code context and structure to AI coding assistants.

Provides comprehensive building and office address information queries for enterprises in China, including fuzzy enterprise search, office address details and statistics, and building information queries to support geographical distribution and occupancy analysis.

An MCP server that uses Google's Gemini API to analyze videos and convert them to text descriptions that Claude Code can understand and act upon.

A quickstart template to build and deploy a remote MCP server on Azure Functions using Python, secured with keys and HTTPS.

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.

This server converts PDFs to markdown with intelligent academic paper detection and dual processing engines (marker-pdf for academic content, PyMuPDF for general documents), supporting URL and local file handling, page extraction, batch processing, and PDF link crawling for Claude Code analysis.

Enables uploading, arranging, merging, and downloading PDFs through an MCP-compatible LLM client by interacting with a PDF Merger API.

Enables AI agents to permanently remove personally identifiable information from PDF documents, returning redacted files with the sensitive text deleted rather than merely covered. Supports scanned documents, multiple languages, entity types, and optional human review.

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

MCP server for generating professional PDFs from structured JSON in AI agents like Claude or Cursor, using pure Node.js with embedded fonts and precision text layout.

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.

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 comprehensive tool server for reading, merging, and extracting content from PDF files via local paths or direct URLs. It enables metadata retrieval, regex searching, and page-specific text extraction with built-in caching and workspace-restricted security.

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

Enables AI agents to securely read and extract information from PDF files including text content, metadata, and page counts from both local files and URLs within the project context.

Empowers AI agents to securely read and extract information (text, metadata, page count) from PDF files within project contexts using a flexible MCP tool.

Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.