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

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

A Model Context Protocol server providing AI assistants with comprehensive project, task, and subtask management capabilities with project-specific storage.

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.

VS Code extension that bridges local Jupyter Notebooks with AI Agents via MCP, enabling direct control of the active notebook tab.

Enables AI agents to securely control a remote Windows/Linux PC via MCP and REST, executing shell commands and driving coding-agent CLIs like Claude, Cursor, and Codex.

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.

Agentic RAG server for document management, hybrid search, question answering with citations, and analytical code execution, powered by LanceDB and Pydantic AI.

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution

Enables a bot to inspect, query, create, edit, and script Roblox Studio instances in token-cheap batches, plus run Luau, playtests, screenshots, and inputs through Roblox's official Studio MCP connection. It runs locally over stdio or an authenticated local HTTP endpoint, returning small structured answers instead of dumping whole games into context.

Enables AI agents to execute terminal commands and Python scripts, search and manage files, and create or delete folders through the Model Context Protocol.

A simple server that acts as a Master Control Program (MCP) for unified interaction with OpenAI and Anthropic (Claude) AI models through a single API endpoint.

Enables deployment of autonomous AI agents with memory and tool execution capabilities through a WebSocket-based MCP protocol. Provides production-ready infrastructure with REST API access, persistent state management, and extensible function registry for building self-hosted AI systems.

Enables AI agents to run shell commands, execute Python code, read and write files, search text, and create or delete folders through MCP stdio tools.

An intelligent codebase processing server that provides agentic RAG capabilities for code repositories, enabling semantic search and contextual understanding through self-evaluating retrieval loops.

A production-ready foundation template for building Model Context Protocol (MCP) servers with FastAPI, featuring modular tools, comprehensive testing, and OpenShift deployment configurations. Includes automated transformation scripts to create custom domain-specific MCP servers.

Delegates coding tasks to the locally installed Kimi Code CLI, reusing its auth, models, and configuration while adding tools for sessions, replies, model discovery, live turn steering, and history access.

An MCP server that offloads bulk coding tasks to local LLMs, allowing Claude Code to delegate repetitive work like boilerplate generation and code polishing while preserving its context for complex reasoning.

Enables AI coding agents to safely execute commands, run tests, and modify project files inside disposable, policy-enforced Docker sandboxes that are isolated from the host machine and its credentials.

An advanced MCP server that provides an agentic interface to Google's Gemini 3.1 models via Vertex AI, combining real-time Google Search, URL analysis, and Python code execution to solve complex multi-step research and data tasks.

Enables AI-powered instant code merging at 10,000+ tokens/sec and agentic codebase search with natural language queries through the Relace API.