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

MCP server for local compression of various image formats

Compression AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

An MCP server that exposes documents.js's document conversion, .odb, metadata, and font tooling as MCP tools, enabling agents to convert, inspect, and edit a wide range of document formats over stdio.

Enables document format conversion between Word, Markdown, PDF, HTML, and plain text, supporting batch operations and format validation via the AI MCP protocol.

MCP server for AI-driven document creation, enabling generation of PowerPoint decks with consistent design themes and auto-generated images via ComfyUI.

A local document evidence layer for MCP clients that ingests documents (PDF, Office, images) with optional OCR, indexes them in SQLite FTS, and provides retrieval tools with source coordinates.

Provides AI agents with multi-format document indexing, hybrid dense and sparse search with reranking, and relational SQL querying over extracted tables.

A local AI document assistant MCP server that enables listing, reading, and editing documents via tools, resources, and prompts, allowing LLMs to manage document workflows through natural language.

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.

Enables AI agents to search, deep-read, and build knowledge bases from Markdown, PDF, DOCX, and PPTX documents via MCP tools for retrieval, document navigation, and ingestion.

Provides AI agents with comprehensive document parsing capabilities including PDF text extraction, OCR, HTML-to-markdown conversion, table extraction, and summarization, optimized for agent workflows.

Converts PDFs, Office files, spreadsheets, emails, audio, and more to Markdown locally, enabling AI assistants to read and process them without cloud upload.

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

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.

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

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.

Enables real-time indexing and semantic search of local documents (PDF, Word, text, Markdown, RTF) using vector embeddings and local LLMs. Monitors folders for changes and provides natural language search capabilities through Claude Desktop integration.

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.

Enables intelligent document processing by extracting text, classifying document types, and generating structured summaries from PDFs and images using vision LLMs.

Enables NPC agents to read, write, search, and manage a shared corporate document store, and to turn Mission Scripts into structured report outlines and scaffolds aligned to strategic intent.