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

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

MCP server for document conversion and processing, enabling AI assistants to handle various document formats.

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.

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

Provides read-only, authenticated access to prepared Markdown, images, document structure, and original-file references published as immutable versioned bundles alongside Paperless-ngx source documents. It can also explicitly forward selected prepared Markdown into WeKnora's existing ingestion interface without subscribing to future documents.

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.

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.

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.

Universal MCP server for extracting text from various document formats including PDF, Excel, Word, CSV, and more, with support for streaming, limits, and markdown conversion.

Enables reading and searching documents in allowlisted folders via LiteLLM and MCP tools, using COM for Office files to support IRM-protected content.

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.

An MCP server that converts and processes document formats such as DOCX and PDF into Markdown, enabling AI assistants to work with document contents.

Enables AI agents and MCP clients to compress retrieved web documents against a query, pruning irrelevant noise through deterministic algorithmic scoring so downstream context stays lean. It exposes this capability over the Model Context Protocol for integration with Claude Desktop, Cursor, and similar clients.