"Techniques for Document Compression and Chunking" matching MCP servers:

A stateless MCP server for composing, validating, auditing, and rendering consequential documents from a small semantic model.

Enables AI agents to efficiently manage and update component documentation with precise partial updates, saving up to 75% tokens compared to full rewrites.

A no-server MCP toolkit that organizes project wikis from Feishu documents, messages, and more, enabling AI clients to answer questions with cited evidence to prevent hallucinations.

Provides access to microCMS documentation, enabling AI assistants to search and retrieve the latest document content.

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

MCP server for managing product documentation as markdown files. Supports creating, reading, updating, deleting, and searching docs with optional semantic search.

Private local stdio MCP server for metadata-only discovery, recording, and querying of Markdown and PDF documents.

Enables clients to inspect local PDFs by searching text, rendering pages, and cropping selected regions at high resolution directly from the source document. It runs locally over stdio without requiring models, embeddings, GPUs, or API keys, and does not modify source PDFs or send network requests.

Provides AI tools with access to ProudNet documentation through search, page retrieval, and section listing capabilities. Enables users to query ProudNet networking library documentation using natural language.

Enables MCP clients to extract text from PDF, image, Word, PowerPoint, and Excel files using MinerU, either on the local machine or a self-hosted private-network API. Parsed results are saved locally as Markdown, structured JSON, and extracted images, which can then be read back in segments.

A sophisticated server that enables AI assistants to automatically analyze codebases and generate comprehensive, professional documentation.

Local document intelligence for AI agents — extract text, detect tables, read metadata, analyze structure, search keywords, and detect language from PDF and DOCX files. No cloud API required, no API key needed.

MCP server for AI-assisted PDF reading with pagination, search, and page navigation.

Enables AI assistants to search and retrieve content from the Chinese Minecraft Wiki, including article introductions, full content, and category lists.

A Model Context Protocol (MCP) server that allows interaction with PDF and EPUB documents, designed to work with Windsurf IDE by Codeium.

A local document processing toolkit for AI agents that extracts text, converts PDFs to Markdown, merges files, extracts tables, and summarizes documents without external API dependencies.

A secure Model Context Protocol server that enables Claude AI to list, search, and read local documents in formats such as PDF, DOCX, and XLSX. It features read-only access with path traversal protection and supports deployment via Docker with SSE or STDIO transport options.

An MCP server that uses the Docling toolkit to convert various document formats, including PDFs, Office files, images, and audio, into clean Markdown for AI processing. It supports multiple processing pipelines like VLM and ASR with intelligent auto-detection and job queue management.

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.

Enables conversion between PDF/Office documents and structured JSON, supporting extraction and generation of document content.