This article explains how to integrate Gitee's Model Control Protocol (MCP) with Java using LangChain4j, covering download, Maven setup, configuration, both stdio and SSE transport modes, sample code, output examples, and a comparison of the two modes to create an AI‑powered repository assistant.
This article explains the Model Context Protocol (MCP) released by Anthropic, describes its three core components, outlines the problems it solves for AI agents, and details four MCP server implementations—github, fetch, sequential‑thinking, and tavily—along with usage commands and code examples.
This article introduces several Model Context Protocol (MCP) services—including Sequential Thinking, Firecrawl, Fetch, Hot News, Playwright, Magic, and Brave Search—provides their GitHub links, detailed Mac and Windows installation commands, and explains how to combine them with a Sequential Thinking prompt to achieve a Manus‑style AI agent workflow.
This article explores how AutoDev can be implemented as an MCP service and client, enabling any AI Agent tool—such as Cursor, Cline, or GitHub Copilot—to invoke IDE capabilities, integrate external tools like Git, Docker, and Kubernetes, and streamline the full software development lifecycle.
The article examines the rapid rise of AI agents like Manus and OpenManus, explains the limitations of cloud‑only models, details the Function Calling mechanism and its pros and cons, introduces the Model Context Protocol (MCP) as a more powerful evolution, and finally describes how AI Agents combine planning, dynamic tool use, memory, and autonomous decision‑making to achieve fully closed‑loop intelligent automation.
This article provides a comprehensive overview of the Model Context Protocol (MCP), explaining its purpose, core components, differences from traditional APIs, detailed architecture, message types, connection lifecycle, error handling, and step‑by‑step instructions for building and using MCP servers to enable AI agents to act on real‑world data and tasks.
The Model Context Protocol (MCP) introduced by Anthropic provides a standardized, TCP/IP‑like communication layer that unifies resources, tools, and prompts, enabling seamless integration of large language model agents with external systems, reducing fragmentation, and accelerating AI agent development.
This tutorial explains how to use Anthropic's open‑source Model Context Protocol (MCP) to connect large language models with external data sources by building both MCP servers and clients in Node.js/TypeScript, covering architecture, transport options, SDK usage, and practical code examples.
The article explains Anthropic’s Model Context Protocol (MCP), a universal interface that lets AI assistants like Claude query and manipulate data sources—including SQLite databases—using plain natural language, and compares it with existing tools such as Chat2DB.
This guide explains how the open‑source Model Context Protocol (MCP) enables natural‑language control of Docker containers, walks through environment setup, Spring AI integration, client project configuration, and demonstrates a full execution log showing automated image pulling, port mapping, volume mounting, and container startup.
This tutorial explains how to integrate Spring AI's Model Context Protocol (MCP) with Chat2DB to enable secure, natural‑language queries against a PostgreSQL database, covering configuration, core Java code, execution flow, and sample SQL queries.
Spring AI now supports the Model Context Protocol (MCP), a universal adapter that lets AI applications seamlessly read databases, files, and APIs, offering a plug‑and‑play alternative to function calling, with simple Maven setup and code examples to get started quickly.
Nacos 1.1.4 introduces Istio MCP integration, a server‑side upgrade guide, client dependency changes, custom instance ID support, and detailed usage steps, while also highlighting community contribution paths and previewing the upcoming 1.2.0 release with permission control.
This article explains how the ZhuangZhuang QA team builds and operates a remote device scheduling workflow on the MCP cloud testing platform, detailing the interaction between the automation test platform, MCP, and Agent services, and presenting three practical device allocation strategies for stable, scalable mobile automation.
