In this we implemented RAG Architecture using MCP

本地优先的多模态视觉 MCP —— 为无视觉 LLM（DeepSeek、GLM 等）提供可自定义端点的看图 / OCR / 文档解析能力。

A comprehensive Model Context Protocol (MCP) server providing advanced access to the UniProt protein database.

MCP-compliant image augmentation server using Albumentations

MCP server for AlbumentationsX image augmentation workflows

This is a local rag with retrieval-augmented experience log for coding with indexed framework, reranking documents, emdedding-normalization

Model Context Protocol server for AlbumentationsX augmentation previews, validation, and segmentation review.

An MCP-compatible image augmentation tool powered by Albumentations. Built for Claude, Kiro, and other AI agents.

Retrieval-augmented research CLI that answers questions using Reddit discussions as evidence — searches threads, ranks the useful comments, and returns cited context. Ships an MCP server.

A Retrieval-Augmented Generation (RAG) Model-Controller-Provider (MCP) server designed to assist AI agents and developers in understanding and navigating codebases.. It supports incremental indexing and multi-language parsing, enabling LLMs to understand and interact with code.

A Perplexity API MCP server that unlocks Perplexity's search-augmented AI capabilities for LLM agents. Features robust error handling, secure input validation, and transparent reasoning with the showThinking parameter.

A Perplexity API Model Context Protocol (MCP) server that unlocks Perplexity's search-augmented AI capabilities for LLM agents. Features robust error handling, secure input validation, and transparent reasoning with the showThinking parameter. Built with

A Perplexity API Model Context Protocol (MCP) server that unlocks Perplexity's search-augmented AI capabilities for LLM agents. Features robust error handling, secure input validation, transparent reasoning, and multimodal support with file attachments (P

🚀 MJOS v2.5.6 - Enterprise-Grade AI Operating System with Enhanced MCP Support & Bug Fixes. Perfect for Claude Desktop, Cursor, VS Code, and Augment. Features: Intelligent Memory System, Team Collaboration, Fixed MCP Tool Context Binding, Zero Resource L

A Perplexity API Model Context Protocol (MCP) server that unlocks Perplexity's search-augmented AI capabilities for LLM agents. Features robust error handling, secure input validation, and transparent reasoning with the showThinking parameter. Built with

ReasoningBank MCP Server - Memory-augmented reasoning for AI agents

A Retrieval-Augmented Generation (RAG) system implemented in Go

A demo of Retrieval-Augmented Generation (RAG) application with MCP server integration.

A TypeScript implementation of a Model Context Protocol (MCP) server, designed to evaluate MCP capabilities over/with RAG (Retrieval-Augmented Generation)

Nuclei API is an advanced, AI-augmented platform for automated vulnerability scanning and template management, built around Nuclei.

📖 A Retrieval-Augmented Generation (RAG) MCP server for markdown documentation with semantic search capabilities

A powerful RAG (Retrieval-Augmented Generation) server with dynamic embedding provider support via the Model Context Protocol (MCP)

Development Governance Platform — deterministic methodology enforcement for AI-augmented teams

A lightweight Model Context Protocol (MCP) server that brings RAG (Retrieval-Augmented Generation) capabilities to your LLM over Markdown documentation stored on S3.

Documentation Augmented Generation (DAG) MCP Server - First multi-ecosystem, version-aware MCP server to reduce AI code hallucinations

Nia is a context-augmentation layer for agents, primarily designed for coding agents. It provides them with an up-to-date knowledge base and improves their performance by 27%.

Retrieval-Augmented Generation (RAG) based Document Assistant that enables users to upload PDF documents and ask natural language questions about their content.

The modern, self-hosted comic book library server, smart reader, and AI-augmented cataloging platform

Teamfight Tactics MCP Server - Connect AI assistants to TFT data via MCP. 10 tools for compositions, augments, ranked stats, and AI coaching.

Nexus-KB is an open-source reference architecture for building a production-grade enterprise Retrieval-Augmented Generation (RAG) and Knowledge Graph (KG) engine.

A powerful RAG (Retrieval-Augmented Generation) system built with LangChain, designed as an MCP server for Cursor, VS Code, and other AI assistants

🤖 A multi-project RAG (Retrieval-Augmented Generation) platform that provides project-aware access to documentation and knowledge through a web dashboard with AI chat, CLI, and MCP server.

An intelligent research assistant that searches, analyzes, and writes academic papers using a Retrieval-Augmented Generation (RAG) pipeline. Built with LangGraph, FAISS vector database,MCP and Google Gemini.

Environnement de travail augmenté pour les assistants IA d'Alter-si : contexte métier, connecteurs d'entreprise et mémoire d'apprentissage

RagWiser is a Retrieval Augmented Generation (RAG) system built with Spring Boot that enables users to upload PDF documents, process them, and ask questions about their content using natural language.

Reusable components and complete chat system to improve Large Language Models (LLMs) capabilities when generating SPARQL queries for a given set of endpoints, using Retrieval-Augmented Generation (RAG) and SPARQL query validation from the endpoint schema.

mcp-rag-server is a Model Context Protocol (MCP) server that enables Retrieval Augmented Generation (RAG) capabilities. It empowers Large Language Models (LLMs) to answer questions based on your document content by indexing and retrieving relevant information efficiently.

A Model Context Protocol server for fetching and storing documentation in a vector database, enabling semantic search and retrieval to augment LLM capabilities with relevant documentation context.

A modular Python framework implementing the Model Context Protocol (MCP). It features a standardized client-server architecture over StdIO, integrating LLMs with external tools, real-time weather data fetching, and an advanced RAG (Retrieval-Augmented Generation) system.

A powerful MCP (Model Context Protocol) server that provides RAG (Retrieval-Augmented Generation) capabilities with support for multiple document formats. Powered by LangChain, ChromaDB, with OpenAI or Ollama embeddings.

Deterministic software attestation for AI-augmented development — witness, probe, and seal any repo with SHA-256. Fleet registry across 131 VERITAS nodes. Agent-native, zero cloud.

An intelligent Model Context Protocol (MCP) server for Azure AI Search integration with Claude Desktop - Transform enterprise document search into natural AI conversations using LangGraph workflows, Google Gemini, and advanced retrieval-augmented generation (RAG).

An intelligent, AI-driven codebase analytics engine powered by Retrieval-Augmented Generation (RAG). It performs precise repository analysis with hybrid search. language-aware AST chunking, exact line-level citations, and MCP (Model Context Protocol) tools for seamless integration with IDEs and AI agents.

A production-ready Retrieval-Augmented Generation (RAG) application built with Streamlit, LangChain, and ChromaDB. Features Model Context Protocol (MCP) integration for seamless tool calling and supports multiple LLM providers including OpenAI and Ollama.

An intelligent HR assistant that orchestrates multiple data sources using Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG) to provide accurate, context-aware responses. Built with Python, LangChain, ChromaDB, Groq, and Streamlit.

RAG-Sandbox is a comprehensive, multi-tenant Agentic RAG (Retrieval-Augmented Generation) platform that redefines how users interact with their data. It moves beyond simple text retrieval to offer a fully multimodal, intelligent experience—transforming static documents into conversable agents, podcasts, and immersive stories.

This project implements a Model Context Protocol (MCP) server that enables AI models to perform semantic search and intelligent document retrieval from local or remote filesystems. The server facilitates RAG (Retrieval-Augmented Generation) workflows, enhancing AI context by retrieving information from diverse sources.

A production-grade Model Context Protocol (MCP) server that augments local LLM inference (via vLLM or Ollama) with real-time external data (e.g., weather, finance, news) to make responses dynamic, accurate, and context-aware. This system is designed for modularity, scalability, and performance in local or hybrid LLM deployments with support for co

A Python-based server implementation of the Model Context Protocol (MCP) designed for Retrieval-Augmented Generation (RAG) workflows. It manages the flow of context, tool invocation and model output in one unified service. Supports modular “Models” and “Modules” directories for extensible integrations, environment configuration, and easy deployment

Secure Retrieval-Augmented Generation (RAG) with role-based access control using Mastra AI orchestration, with JWT Secure, also Deep Research built with Cedar & Next.js. Gemini, OpenAI, Openrouter, & Gemini CLI usable. Also exposes tools, agents as MCP Server Also A2A Agent Cards

Graph-Augmented Code Intelligence RAG engine pairing deterministic AST call graphs with dense vector search to boost direct-callee Recall@8 from 0.0% to 53.3% (+53.3 pp) under a 5.8ms p95 latency bound. Powered by a QLoRA fine-tuned Qwen2.5 adapter (70.02 CodeBLEU), quality verification harness, and production Model Context Protocol (MCP) server.

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MCP server for managing, analyzing, and optimizing computer vision datasets

A code repository indexing tool to supercharge your LLM experience.

Local-first AI knowledge layer for Collibra documentation via MCP

MCP server for docs2db-api RAG queries

Rimworld Coding RAG MCP server

Local code intelligence that reads your entire codebase. Secrets stay on your machine, period.

五层认知记忆引擎 — 感知→内化 + 混合检索 + 遗忘曲线 + 知识图谱 + FSRS间隔重复

Local-first AI knowledge layer for ServiceNow development