"Information about RAG" matching MCP servers:

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

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

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).

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.

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.

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

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 durable DAG-based task planner exposed as an MCP server that lets AI orchestrators break a goal into a dependency graph of tasks, execute them in parallel where possible, track state durably, and handle human-in-the-loop approval through 22 MCP tools.

MCP server for integrating manufacturing systems (MES/ERP/quality/maintenance) with LLM agents, enabling event ingestion, incident triage, approval workflows, and RAG-based knowledge retrieval.

Enables AI agents to interpret Form.io form schemas and automate structured data collection and agentic workflows through the Model Context Protocol.

Enables running durable, traceable AI agents via LangGraph through a universal MCP interface, integrating with Hatchet for orchestration, logging, and retries. Provides tools for knowledge management (ingestion, RAG) and Kubernetes operations (diagnosis, auto-fix).

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

A server that enables AI models to access user geolocation information via EdgeOne Pages Functions through the Model Context Protocol.

A transparent message ferry between Claude Desktop and Claude Code CLI, enabling task delegation with any MCP configuration without the bridge needing to know about specific MCPs.

MCP server that provides 7 specialized agents with RAG knowledge bases, allowing Claude to search and ask domain-specific questions and route tasks to the appropriate agent without API keys.

An authenticated MCP gateway that ingests documents and orchestrates hundreds of tools via progressive discovery, keeping context cost constant. It provides per-user RAG over ingested documents and a 116-tool registry that the model navigates through search, describe, and invoke tools.

MCP server that coordinates multiple AI specialists (SQL database, document RAG, and web research) using LangGraph, routing each question to the relevant agents and synthesizing their responses into a single answer with honest confidence flags.

Enables autonomous planning by decomposing prompts into directed acyclic task graphs and resolving dependency topologies for multi-agent pipelines.