"Agentic RAG: Understanding or Exploring Its Meaning and Applications" matching MCP servers:

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

Read-only MCP server that exposes the agentic-os governance, SDLC, and Quality Engineering methodology to any MCP host. It never writes to your repository and never executes code — it serves the methodology, plans an install, and verifies it, handing any commands back to the host to run.

MCP server that initializes, manages, and operates traceable agentic infrastructure across your digital ecosystem, serving as a control plane to know project states, health, and initialize missing setups.

A persistent, cross-referenced engineering knowledge base for AI agents, with MCP tools for wiki knowledge, agent runtime, and repository context. Preserves sources and citations, surfaces knowledge-quality issues, and PIN-gates private scopes and write/admin operations.

A modular backend that connects language models to real-world tools like Gmail and Google Docs via MCP server abstractions, enabling agentic workflows with LLM reasoning and secure OAuth integration.

Lets many AI agent sessions share one real logged-in browser, each with its own tab group, using standard Playwright MCP tools.

An MCP-based tool orchestrator that exposes a single execute_task tool to Claude while internally managing 100+ tools through hierarchical navigation with a cheaper LLM, preventing context overflow from loading all tool definitions.

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.

Enables any MCP client to drive a multi-agent orchestration engine with planning, specialist tools, critic revision, and human-in-the-loop approval for sensitive actions.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

A communication framework that enables multiple AI agents to collaborate through asynchronous messaging, agent registration, and discovery. It facilitates complex task coordination and real-time discussion across distributed agent workflows using the Model Context Protocol.

AI-native orchestration layer with 80+ tools for task management, code editing, browser automation, terminal control, and persistent memory across CLI, local MCP, and cloud deployments.

Enables MCP clients to interact with a safe, orchestrator-driven multi-agent builder that enforces governance constraints such as no deployment, no secret leakage, and grounded, non-hallucinated answers through an always-on governance gate.

Enables autonomous hierarchical multi-agent orchestration for MCP, with persistent SQLite state, real-time web monitoring, and support for mixing multiple CLI and direct API execution providers.

A local MCP gateway that allows AI clients to manage and interact with multiple MCP servers through a single connection, providing token-efficient access to tools and resources.

Enables multiple agents to share a single Chrome browser with isolated tabs and shared sessions, allowing coordinated browsing and automation.

Enables MCP clients to read, summarize, create, update, and delete tasks on a team task board according to the caller's tenant and role. It also supports running the same board operations exposed to forms, JSON routes, Artisan, and the copilot.

A self-hosted Microsoft Teams MCP server that serves as an agnostic human-in-the-loop gateway for AI agents.

Enables an AI agent to observe, plan, and act inside a real browser session by exposing navigation, snapshot, click, type, and screenshot MCP tools over local JSON-RPC and SSE. It runs a Playwright-backed browser in-process and provides stable element references for reliable actions.

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