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A simple MCP server that implements a note storage system with RAG capabilities, allowing users to store notes and generate summaries of stored content.

A lightweight server that provides persistent memory and context management for AI assistants using local vector storage and database, enabling efficient storage and retrieval of contextual information through semantic search and indexed retrieval.

Ragdb MCP

MCP対応のRAGシステム。Markdownドキュメントをベクトル化し、自然言語で高速検索。LibSQL、Qdrant、PostgreSQLに対応してます。

Integrates RAGFlow's knowledge base API with Claude Desktop for document retrieval, semantic search across multiple datasets, and intelligent query refinement using DSPy.

Provides a comprehensive Model Context Protocol interface for RAGFlow, enabling AI models to perform semantic retrieval, manage datasets, and handle document chunks. It supports advanced features like GraphRAG and RAPTOR for sophisticated knowledge base management and natural language querying.

A fixed MCP server that interacts with RAGFlow for dataset management and chat operations, handling legacy endpoint errors and providing a fallback to OpenAI-compatible endpoints.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

RAG Knowledge Graph - MCP server providing AI-powered tools and automation by MEOK AI Labs

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

MapRag is a discovery + routing layer for retrieval. It indexes RAG-capable MCP servers, enriches them with structured metadata, and helps agents (and humans) quickly find the right retrieval server for a task under constraints like citations, freshness, privacy, domain, and latency. MapRag does not do RAG itself. It helps you choose the best RAG tool/server to do the retrieval.

Enables semantic search across text documents using vector embeddings stored in PostgreSQL. Provides multiple search modalities including semantic similarity, question/answer, and style-based search through a retrieval-augmented generation system.

A server that integrates Retrieval-Augmented Generation (RAG) with the Model Control Protocol (MCP) to provide web search capabilities and document analysis for AI assistants.

A Model Context Protocol server that exposes Retrieval-Augmented Generation capabilities and a weather tool, allowing clients to interact with document knowledge bases and retrieve weather information.

Implements a RAG workflow that integrates with any custom knowledge base and can be triggered directly from the Cursor IDE.

An API that enables document querying through a Retrieval-Augmented Generation system implemented with Memory-Controller-Policy architecture for improved maintainability and scalability.

A pluggable, observable modular RAG service framework that exposes tool interfaces via the MCP protocol, enabling AI assistants like Copilot and Claude to directly query knowledge bases.

A local command-based assistant that enables users to control system tools, play YouTube music, and search various websites using Playwright automation. It supports tasks ranging from media playback and browser navigation to file system operations and system power management.

Provides intelligent retrieval capabilities for local files by scanning directories, generating vector indexes, and enabling semantic search through RAG (Retrieval Augmented Generation) with incremental indexing support.

Search your knowledge bases from any AI assistant. Ragora is a knowledge marketplace that lets you upload, organize, and search documents using hybrid RAG (dense + sparse vectors). Connect via MCP to search across all your collections directly from Claude, Cursor, VS Code, and more.

Serverless document and media processing with AI chat. Upload documents, images, video, and audio — extract text with OCR or transcription — query using Amazon Bedrock.