"Information about RAG (Retrieval-Augmented Generation)" matching MCP servers:

Enables AI assistants to perform retrieval-augmented generation with hybrid search, reranking, multi-modal image processing, and RAG evaluation through standardized MCP tools.

An agentic AI system that orchestrates multiple specialized AI tools to perform business analytics and knowledge retrieval, allowing users to analyze data and access business information through natural language queries.

Connects a RAG application to open-webui using Model Context Protocol (MCP), enabling server-to-client communication for context retrieval and tool usage in remote environments through Server-Sent Events (SSE).

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

MCP server providing tools for entity extraction, query refinement, and relevance checking to build Agentic RAG applications.

An MCP proxy that embeds tool definitions and returns only the top-k semantically relevant tools per query, reducing context usage and routing calls to upstream MCP servers.

Orchestrates multi-stakeholder feature refinement and development execution workflows for AI-assisted software teams.

Enables personalized AI tutoring by allowing students to upload PDF/DOCX study materials that are processed and indexed for semantic search. Provides intelligent responses based on the student's own learning materials using RAG technology.

An open-source platform for Retrieval-Augmented Generation (RAG). Upload documents and query them ⚡

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

An open protocol server that implements Anthropic's Model Context Protocol to enable seamless integration between LLM applications and RAG data sources using Sionic AI's Storm Platform.

Search and discover AI agents, skills, prompts, bundles and MCP connectors from a curated catalog of 4500+ assets. Provides tools for searching, browsing categories, and accessing detailed information about each asset.

Connects Claude Code to the Perplexity Comet browser to enable autonomous web browsing and agentic research. It features multi-tab management, smart completion detection, and a specialized tool set for organized information retrieval.

Provides AI assistants with real-time access to the AGNO framework documentation by enabling them to browse, search, and fetch documentation pages. This server allows users to query information about AGNO's Agents, Teams, and Workflows directly through MCP-compatible clients.

A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.

A production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.

The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.

A Model Context Protocol server that allows AI assistants to interact with the VRChat API, enabling retrieval of user information, friends lists, and other VRChat data through a standardized interface.

A Model Context Protocol server that enables large language models to interact with Apache Superset databases through REST API, supporting database queries, table lookups, field information retrieval, and SQL execution.

A Model Context Protocol server that runs on Cloudflare Workers with OAuth login, allowing clients like Claude Desktop to connect to it for tool-augmented AI interactions.