Haiku RAG is an intelligent retrieval - augmented generation system built on LanceDB, Pydantic AI, and Docling. It supports hybrid search, re - ranking, Q&A agents, multi - agent research processes, and provides local - first document processing and MCP server integration.

DevDocs is an intelligent document crawling and processing tool designed to help developers quickly understand and implement technical documents, significantly shortening the R & D cycle.

HWP-MCP is a protocol service that enables AI models to control HWP documents, supporting document creation, editing, and automation processing.

The Pinecone MCP Server is a model context protocol server designed for Claude Desktop, providing read and write interaction capabilities with Pinecone indexes.

Claude Talk to Figma MCP is a plugin based on the Model Context Protocol that allows AI tools such as Claude Desktop to directly interact with Figma, enabling AI-assisted design capabilities. The project connects the AI client and the Figma plugin through a WebSocket server, supporting design operations such as document analysis, element creation and modification, and text processing.

An MCP server for obtaining and searching third - party software package documentation, supporting multi - source scraping, intelligent processing, and hybrid search.

The enhanced Markdownify MCP UTF-8 is a Markdown processing service that supports multilingual content conversion. It optimizes UTF-8 encoding support, provides Markdown conversion capabilities for various formats such as PDF, images, audio and video, and Office documents, and is specifically optimized for the Windows system.

LegalContext is an open - source MCP server that securely connects the Clio document management system with the Claude AI assistant, enabling local legal document processing and retrieval.

The FHIR MCP Server is a medical data management server based on the FastMCP framework, providing a complete implementation of the Model Context Protocol (MCP) and supporting the interaction between LLM agents and FHIR-compatible backends. It provides standardized interfaces, supports comprehensive CRUD operations on FHIR resources, and implements intelligent document processing and semantic search through a series of tools.

A Python tool for converting CHM help files to Markdown format, supporting code highlighting, table preservation, and batch processing, facilitating version management of technical documents.

A high-performance PDF to Markdown service based on MCP, supporting batch processing of local files and URLs, retaining the document structure and intelligently optimizing the output.

The Feishu MCP Plugin is a service that connects Feishu with AI coding tools (such as Cursor), providing functions such as document reading, message sending, and contract approval, and supporting operation through self - built application authorization.

ChatPPT is an AI - driven presentation generation service that supports generating PPTs according to themes or requirements, automatically creating PPTs by uploading documents, and provides online editing and download functions. Its MCP server supports 18 intelligent document processing APIs, covering various scenarios such as PPT production and resume analysis.

The MCP server for handwritten OCR, providing functions such as document upload, status check, and text retrieval

This project is a document processing server based on the Model Context Protocol (MCP) standard. By building a vector database and an MCP interface, it enables AI assistants to access external document resources and break through the knowledge limitations of large language models. The project includes two major components: a document processing pipeline and an MCP server. It supports multiple embedding models and file formats and can be applied to scenarios such as querying the latest technical documents and understanding private code libraries.

The Feishu MCP Server is a service based on the Model Context Protocol that provides Feishu API integration, enabling AI models to easily interact with Feishu services.

A document indexing system based on PostgreSQL and pgvector, supporting AI - driven semantic search, including functions for crawling, processing, and querying documents.

A documentation management system based on the Model Context Protocol (MCP), providing functions such as resource templates, documentation processing, task management, and integration testing.

An MCP server that provides unstructured document processing capabilities, supports multiple file formats, and helps LLMs extract and utilize document content.

Zed's PDF semantic search extension, integrating an AI assistant to enhance document processing capabilities

Bookworm is a toolset for processing docs.rs documentation, including functions such as downloading, indexing, and querying documentation, and provides an MCP service interface for LLM calls.

A high-performance local RAG system based on Rust, integrated with Claude Desktop via the MCP protocol to achieve local processing, semantic search, and privacy protection of PDF documents.

The Insights Knowledge Base (IKB) MCP Server is a free, plug-and-play knowledge base project with over 10,000 high-quality insight reports built-in. It supports local secure storage and private document processing. The project requires no configuration, is free forever, and is committed to updating report resources weekly.

The official Feishu/Lark OpenAPI MCP tool encapsulates platform API interfaces and supports AI assistants to directly call and implement automated scenarios such as document processing and session management

The Dart MCP Server is an implementation of the model context protocol (MCP) server based on Dart, providing functions for task management, document processing, and space organization, and supporting integration with AI assistants.

The MCP Document Indexer is a Python - based local document indexing and search server that uses the LanceDB vector database and a local LLM (through Ollama) to implement real - time monitoring, multi - format document processing, and semantic search, and provides tools for AI assistants such as Claude through the Model Context Protocol (MCP).

An enhanced Readwise MCP server that integrates Readwise Reader document management and a complete highlighting function, providing intelligent text processing and context - optimized responses, and is compatible with all MCP client tools.

This project provides an OpenAPI specification processing server based on the MCP protocol, which supports dividing large OpenAPI files into multiple small files or extracting specific interfaces to generate new files, facilitating programmatic operations on API documentation by clients such as AI assistants.

An AI-based Word document processing service based on FastMCP

The Mapbox Developer MCP Server enables AI assistants to directly access Mapbox developer APIs, helping developers build map applications more efficiently. It supports functions such as style management, token management, document query, and geographic data processing.

An MCP server for document retrieval and processing based on vector search, providing document enhancement functions for AI assistants

The Upstage MCP Server is a server that connects AI assistants with the Upstage AI document processing API. It supports extracting structured content from PDFs, images, and Office files and integrates with MCP clients such as Claude Desktop.

A repository containing a variety of practical MCP services, providing services such as Git, Google Sheets, Jenkins, and file operations, along with development tutorials

DocNav is a document navigation server based on the MCP protocol, providing LLM agents with human-like long document reading, analysis, and navigation capabilities, supporting multi-format document processing and intelligent search functionality.

An MCP server that provides comprehensive document processing capabilities

A Docx document processing service based on the FastMCP library, supporting the creation, editing, and management of Word documents through an AI assistant

DocNav is a document navigation server based on the MCP protocol, providing LLM agents with human - like document reading, analysis, and navigation capabilities, supporting multi - format document processing and intelligent search.

The RAG Document MCP Server is a vector search-based document processing tool that provides document retrieval and context enhancement functions for AI assistants.

A privacy - first document search server that runs entirely locally, providing semantic search functions for AI programming tools through the MCP protocol. No API keys or cloud services are required, and all data processing is completed on the user's computer.

A Python-based document processing and Retrieval Augmented Generation (RAG) server that uses the DuckDB database to store embedding vectors, supports multiple file format processing, and provides a Web interface and API.