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Converts DOCX files to Markdown with formatting preservation and image extraction, and provides image analysis tools for document processing workflows.

Enables parsing and extraction of content from various document formats (PDF, Word, Excel, PowerPoint) into Markdown format using the Niutrans document API.

An intelligent document processing system that automatically classifies, extracts information from, and routes business documents using the Model Context Protocol (MCP).

A multi-format MCP server that enables reading and generating Office, PDF, text, EPUB, and presentation documents. It provides unified tools for document processing and creation through AI assistants.

Efficiently delivers project documentation to AI agents like Claude on-demand, optimizing token usage by loading context only when needed. Supports document retrieval, listing, and keyword search with security features.

Fetches web pages and converts them to clean, readable markdown format by extracting main content while removing navigation, ads, and other non-essential elements to minimize token usage.

A beginner MCP server that enables Claude to read local text, CSV, and Markdown files. Built as a learning project to understand how to connect AI to local file systems using the MCP protocol.

Automatically crawls documentation websites, converts them to organized markdown files, and generates condensed cheat sheets. Intelligently categorizes content into tools/APIs and provides local-first access to downloaded documentation.

Enables indexing and retrieving notes with full-text search using SQLite, plus building knowledge graphs to find relationships between concepts. Supports natural language note management, tagging, and semantic connections.

A Model Context Protocol server that allows AI assistants and applications to access IQ.wiki data, enabling retrieval of specific wikis, user-created wikis, user-edited wikis, and detailed wiki activities.

A Model Context Protocol server that integrates with Atlassian's Jira and Confluence, enabling AI assistants to interact with these tools directly through features like issue management, page creation, and content search.

A local document processing server that can index various document formats (PDF, DOCX, TXT, HTML) and answer questions based on their content using the Model Context Protocol.

A Streamlit-based web application that generates personalized learning paths by integrating with YouTube, Google Drive, and Notion services through the Model Context Protocol.

An MCP server that enables users to fetch and audit documentation from user-defined llms.txt index files. It provides tools to list documentation sources and retrieve content from specific URLs with built-in domain access controls for secure context retrieval.

Provides controlled access to llms.txt documentation files through MCP tools, allowing AI assistants to fetch and read documentation from user-approved domains with full audit visibility of tool calls and context retrieval.

Enables MCP host applications to retrieve and process structured documentation from user-defined llms.txt files. It provides tools to fetch specific URLs and audit the documentation context returned to the LLM.

This MCP server provides a tool to generate manual test cases in Markdown or CSV format from documentation files and custom rules. It supports text and PDF inputs and can leverage LLM sampling to automate the creation of detailed test scenarios.

A Model Context Protocol (MCP) server that gives your AI assistant the power to convert Markdown into 14 professional document formats — PDF, DOCX, HTML, LaTeX, CSV, JSON, XML, XLSX, RTF, PNG, and more. Stop copy-pasting. Let the AI do the exporting.

Automatically transforms markdown templates (like GitHub Issue templates) into MCP tools and FastAPI endpoints. Load templates from local files, directories, or URLs to create typed API endpoints with Swagger UI documentation.

An MCP server that provides tools for analyzing, linting, formatting, and generating Markdown content. It enables users to programmatically manage Markdown files through features like table of contents generation, statistics calculation, and JSON-to-table conversion.