docshelf-mcp manages AI-friendly document collections — converts PDFs and Markdown into chapter-split shelves with a single navigation INDEX, so AI agents can fetch only the relevant section by raw URL instead of choking on a 4 MB datasheet. Repo: https://github.com/ignatenkofi/docshelf-mcp
An MCP server that enables searching for Rust crates and their documentation from docs.rs, allowing AI agents to find required crates and access the latest documentation.
Generated content to Google Docs to WordPress while preserving formatting and structure for a streamlined content workflow.
A TypeScript-based document processing server that supports various document formats (.docx, .pdf, .xlsx) and integrates with Model Context Protocol SDK for efficient document context management.
A comprehensive document analysis server that performs sentiment analysis, keyword extraction, readability scoring, and text statistics while providing document management capabilities including storage, search, and organization.
Enables AI assistants to fetch, index, and perform semantic RAG-based searches on API documentation from various sources. It provides tools for hybrid search and collection management, allowing users to access up-to-date documentation from projects like Gemini and FastMCP.
Enables AI assistants to generate professional documentation using structured templates based on the POWER framework. Provides access to standardized templates for README, architecture, API, components, and schema documentation.
Enables AI assistants to navigate and query hierarchical documentation structures, supporting markdown files with YAML metadata and OpenAPI 3.x specifications. It features intelligent full-text search, metadata filtering, and a built-in web interface for both human and AI-driven documentation access.
A Model Context Protocol server that enables intelligent searching across documentation for 30+ programming libraries and frameworks, fetching relevant information from official sources.
Document Comparison AI - MCP server providing AI-powered tools and automation by MEOK AI Labs
Enables conversion between multiple document formats including Markdown, HTML, TXT, PDF, and DOCX with automatic format detection. Supports high-fidelity document transformation while preserving content integrity.
Converts Markdown text to Word documents via HTTP API, providing temporary download links with automatic file cleanup after 2 hours.
Extracts and stores documentation content from Microsoft Learn and GitHub URLs into PocketBase with full-text search, metadata preservation, and automatic collection management for easy retrieval and organization.
Enables AI agents to generate professional Word and PDF documents with support for Markdown, syntax highlighting, and smart pagination. It features automatic JSON detection and responsive A4 formatting for creating high-quality technical reports and manuals.
Provides tools and resources for managing in-memory documents, allowing users to read, edit, and list document contents via the Model Context Protocol. It includes features for formatting documents to Markdown and accessing document-specific resources.
Enables systematic document organization with PDF-to-Markdown conversion, intelligent categorization, and automated workflow management. Supports project documentation standards and provides complete end-to-end document processing pipelines.
Enables reading and processing various document formats including Word, PDF, RTF, and text files. Supports extracting media elements like images and links, with features for PDF page range selection and automatic text encoding detection.
A FastMCP-powered microserver that allows users to programmatically generate well-formatted .docx documents with consistent styling, including features like titles, paragraphs, headings, citations, and footers.
A comprehensive Model Context Protocol server that processes Microsoft Word documents with full formatting support, enabling text extraction, HTML/Markdown conversion, structure analysis, and image extraction.
Enables Word document generation from templates using Jinja2 syntax and parsing of DOCX, PDF, and Excel files to extract structured content, metadata, and text.
