This article reviews practical Model‑Chat Plugins (MCP) that front‑end developers can integrate into their workflow—covering Figma, Apifox, Context7, and Chrome DevTools MCPs—explaining setup steps, code examples, and how they dramatically improve efficiency and code maintainability.
This article introduces the Model Context Protocol (MCP) and Large Language Models (LLM), explains their core concepts, transmission mechanisms, lifecycle, and essential modules, and provides step‑by‑step code examples for creating an MCP server, adding tools, resources, prompts, and debugging workflows to accelerate AI‑driven development.
The article explains how the SOFA AI Gateway, built on the open‑source Higress kernel, evolves traditional API gateways into specialized AI gateways by adding intelligent routing, model proxy, agent proxy, and MCP market features to meet the unique latency, resource, and security demands of AI workloads.
This article explains how, amid the rapid rise of AI‑assisted programming, a scientific and actionable R&D efficiency metric framework was designed, detailing core indicators such as AI code adoption rate, data collection methods, platform architecture, and practical insights from a large‑scale implementation.
This article explains the Model Context Protocol (MCP) – an open, universal standard that lets large language models seamlessly interact with external tools and data – covering its core architecture, why it’s needed, underlying principles, tool‑selection mechanics, a step‑by‑step Python server implementation, and practical usage tips.
Anthropic’s recent addition of Skills to Claude introduces a modular, metadata‑driven approach that lets AI agents load specific capabilities on demand, offering a more efficient alternative to MCP’s context‑heavy tool integration and promising scalable, progressive disclosure for specialized workflows.
This article explains the limitations of the traditional HTTP+SSE transport for MCP, introduces the new MCP Streamable HTTP protocol with its unified endpoint, on‑demand streaming and session recovery features, and shows how it improves connection count, success rate, latency, and overall reliability for serverless function compute workloads.
AipexBase, the first Chinese open‑source AI‑native backend‑as‑a‑service platform, lets developers and AI agents bypass traditional server, database, and authentication code by using an MCP‑compatible SDK, with step‑by‑step deployment, API key generation, and integration examples that demonstrate end‑to‑end intelligent app creation.
This tutorial shows how to let large language models autonomously invoke external services—specifically a MySQL database—by using the Model Context Protocol (MCP), covering environment setup, MCP service implementation, agent integration, and real‑world execution results.
The Higress 2.0 release introduces 30 changes—13 new features such as a RAG MCP server and ECDS‑based configuration refactor, 7 bug fixes, 5 refactorings, documentation updates and a test improvement—providing developers with enhanced knowledge‑management capabilities, more stable routing, and clearer documentation for cloud‑native service‑mesh environments.
This article explains how LApiGateway leverages the Model Context Protocol (MCP) to enable AI clients to access existing HTTP RPC services without code changes, detailing the protocol basics, current challenges, the gateway's architecture, implementation steps, and the resulting operational benefits.
The Model Context Protocol (MCP) introduced by Anthropic addresses the scalability, integration, and context‑transfer limitations of traditional Function Calling by offering a standardized, bidirectional, and context‑aware communication layer that simplifies tool discovery, security, and workflow orchestration for LLM‑driven agents.
This article details how an international advertising platform team integrated AI across the entire development pipeline—using AI‑generated documentation, code, rule‑based IDE guidance, MCP servers, and memory banks—to transform repetitive tasks into automated processes, dramatically boosting productivity and reducing manual effort.
This guide explains how to install, configure, and run the Webman MCP plugin—covering features, command‑line tools, component creation, and deployment steps—to rapidly set up a Model Context Protocol server using PHP.
The article explains how the emerging Model Context Protocol (MCP) for AI tools lacks visibility, outlines security and monitoring challenges, compares alternative tracing methods, and presents MCPSpy—a Linux‑only eBPF‑based, non‑intrusive solution that captures MCP stdio traffic, parses JSON‑RPC messages, and outputs human‑readable or JSON logs.
The article analyzes the security challenges introduced by the open Model Context Protocol (MCP) ecosystem, outlines typical attack vectors such as command‑execution hijacking and indirect prompt injection, and presents MCPScan—an open‑source scanner that combines static taint analysis with LLM‑driven reasoning to detect exploitable tool chains before deployment.
This article introduces the Model Context Protocol (MCP) Go SDK v1.0.0, explains its stable API guarantees, provides quick‑start examples for building MCP servers and clients in Go, and outlines the SDK's modular design and error‑handling strategy for AI‑enabled backend development.
OpenAI will unveil Agent Builder at DevDay, a visual drag‑and‑drop canvas that lets users assemble AI agents from templates, logic nodes, connectors and tools, aiming to lower the barrier for creating production‑grade AI solutions and compete with automation platforms like n8n and Zapier.
Facing page white‑screens, slow first‑paint metrics, and endless compatibility screenshots, developers can now use Chrome DevTools MCP—a new AI‑driven protocol that packages all DevTools capabilities into callable interfaces—to automate debugging, performance analysis, and testing directly from editors like Trae and VSCode, boosting efficiency threefold.
This guide explains how to address high deployment costs, slow iteration, and poor manageability of MCP Server by using Alibaba Cloud Function Compute for serverless execution and MSE Nacos Enterprise for automatic registration, dynamic configuration, and unified service governance.
This article explains how AutoDev adds support for Google’s Agent‑to‑Agent (A2A) protocol, detailing its architecture, integration with the Model Context Protocol (MCP), configuration steps, debugging tools, and the benefits of a modular, open‑source AI programming ecosystem.
This article explains how AI agents work, examines OpenManus’s comprehensive tool framework, reviews Gemini CLI’s minimalist tool scheduling and error handling, and discusses Shopify Sidekick’s scaling challenges and Just‑in‑Time instruction strategy, offering practical guidance for building robust, production‑ready agentic systems.
This article explains how the Model Context Protocol (MCP) bridges large language models and external services by converting OpenAPI specifications into callable tools, covering generation with openapi‑generator, mapping rules, three runtime modes (stdio, streamable, SSE), and implementation details in Java, Python, and Go.
This comprehensive guide walks you through ClaudeCode, an Anthropic command‑line AI programming assistant, covering its core concepts, step‑by‑step installation, Chinese‑specific configuration, essential commands, custom agents, hooks, MCP server setup, workflow modes, and additional tools to transform ClaudeCode into a powerful end‑to‑end development partner.
This article examines the security challenges of the Model Context Protocol (MCP) in AI applications, analyzes attack surface expansion across creation, runtime, and update phases, and presents a comprehensive AI‑enhanced scanning architecture with mitigation strategies to protect the entire AI ecosystem.
This article explains how BaikalDB leverages the Model Context Protocol to let large language models query and analyze its distributed database without writing any code, detailing the protocol’s principles, server components, transport methods, tool capabilities, use cases, configuration, and future challenges.
This guide walks you through installing Ollama, pulling the open‑source DeepSeek‑R1:1.5B model, wrapping it with a Go‑based Model Context Protocol (MCP) server, creating a client example, and enhancing the experience with Open‑WebUI while offering performance‑tuning tips.
This tutorial walks through setting up a local TiDB MCP Server, configuring an AI client, and using natural‑language prompts to query TiDB databases, showcasing practical AI‑driven data access and highlighting real‑world application scenarios.
This tutorial walks you through installing Cherry Studio, configuring Baidu and Amap MCP servers with JSON settings, obtaining the required API keys, and using the built‑in AI model to perform route planning, all without writing a single line of code.
This tutorial introduces Claude Code’s Model Context Protocol (MCP), compares it with traditional function calling, explains its client‑server architecture, and provides a step‑by‑step hands‑on guide—including Figma token acquisition, server setup, and Flutter code generation—to demonstrate how MCP streamlines AI‑driven development.
MCP, a protocol from Anbhropic, standardizes how large language models communicate with external tools, databases, and APIs through a client‑server architecture, offering three communication modes (Stdio, HTTP with SSE, Streamable HTTP) and enabling use cases such as intelligent analytics, knowledge hubs, AI chatbots, BPM, API integration, automated testing, and programming assistance.
OpenAI’s new ChatGPT Developer Mode, now supporting the Model Context Protocol (MCP) with read‑write capabilities, lets developers activate connectors to update systems, trigger workflows, and orchestrate multi‑step automation directly from chat, transforming the assistant into a powerful enterprise automation platform.
This article debunks common misconceptions about the Model Context Protocol (MCP), explains its client‑host‑server (CHS) architecture, shows how the Host drives AI decisions while Server and Client remain model‑agnostic, compares MCP with Function Calling, analyzes SDK source code, evaluates practical trade‑offs, and outlines the true engineering value and costs of using MCP in AI applications.
This tutorial walks through setting up a TiDB MCP Server, configuring it with AI tools like CodeBuddy, and using natural‑language commands to query TiDB databases, highlighting deployment steps, available tools, and practical application scenarios.
This article explains the Model Context Protocol (MCP) as an open standard for secure AI‑tool interaction, details its client‑server architecture, showcases a Figma‑focused MCP implementation with code examples, and discusses data simplification, transformers, and image handling that enable AI assistants to generate accurate front‑end code from design files.
This article defines the Model Context Protocol (MCP), explains why it is needed to unify LLM and external tool interactions, details its client‑host‑server architecture, compares its communication modes, outlines Java/Spring AI support, and discusses current adoption trends and open challenges.
On September 5, 2025 the PHP Foundation announced the launch of an official PHP Model Context Protocol (MCP) SDK, detailing its purpose to standardize AI integration, its collaboration with existing community projects, key features like framework support and cross‑platform compatibility, and the early development milestones achieved.
The article explains MCP's three communication modes—stdio, SSE, and Streamable HTTP—focuses on the Streamable HTTP protocol introduced in 2025, and walks through its four lifecycle phases with detailed JSON‑RPC request and response examples for initialization, runtime operations, updates, and closure.
This article explains the Model Context Protocol (MCP) introduced by Anthropic, compares it with Function Call, details its architecture and communication flow, and provides a step‑by‑step Spring AI guide—including server and client development, configuration, code examples, and testing—so developers can integrate AI models with back‑end services efficiently.
This guide explains what the Model Context Protocol (MCP) is, lists key MCP resources, and provides a step‑by‑step tutorial for creating a token, linking a Figma design, configuring Cursor, and using MCP to automatically generate front‑end HTML and Tailwind CSS from a Figma file.
This article explains the Model Context Protocol (MCP) architecture, demonstrates how to build an MCP service for fetching Figma design data, details the data simplification pipeline, and shows how AI assistants can use standardized, secure context to generate accurate code from design files.
This article explains the Model Context Protocol (MCP), its three‑layer architecture, its core advantages, and step‑by‑step development of an MCP server in TypeScript (with Python and C++ examples), showing how LLMs can invoke tools for tasks like Unreal Engine code analysis.
This guide demonstrates using the Tongyi Lingma AI agent to automatically design an SQLite database schema, generate SQL, insert sample data, and conduct data analysis with visual charts, covering all required installations, configurations, prompts, and code snippets.
This article introduces the rapidly popular Gemini CLI, compares it with Claude Code, explains its core features, demonstrates coding, multimodal, and MCP use cases, and details the author’s Easy LLM CLI fork that enables custom model integration, flexible configuration, and direct code embedding for developers.
This article explores how integrating AI agents with the Model Context Protocol (MCP) and tools like Playwright can automate reporting and batch task creation, detailing practical implementations, challenges, performance comparisons with traditional solutions, and best practices for combining AI and engineering to achieve efficient, reliable business workflows.
This article explores the practical integration of AI agents with Model Context Protocol (MCP) to build a smart reporting assistant and automate batch task creation, detailing the technical workflow, tool‑calling capabilities, implementation steps, challenges faced, and the benefits of combining agents with traditional engineering systems.
This guide walks through installing Tongyi Lingma, generating a Qwen3‑based conversational and poetry‑creation agent with Chainlit, integrating Alipay MCP for payment requests, troubleshooting common issues, and providing useful resource links for a complete AI‑agent development workflow.
This article examines the OAuth security challenges in Volcano Engine's Model Context Protocol (MCP) ecosystem and outlines a comprehensive, three‑stage defense strategy—including pre‑authorization double confirmation, token identity isolation, and API‑level permission controls—to protect user assets and data.
This guide walks you through the fundamentals of OAuth 2.0, explains how the MCP protocol supports it, and provides a complete Python example that integrates Google OAuth 2.0 into an MCP server and client, covering configuration, code implementation, and testing procedures.
This guide walks you through Claude Code’s high‑impact features—including Bash mode, auto‑accept edits, plan mode, MCP server management, message navigation, code rollback, and version upgrades—showing how each shortcut saves time, reduces token usage, and streamlines AI‑assisted development.
Learn how to quickly set up OxyGent, a flexible AI agent framework, by installing Python, Node.js, and the MCP tools, configuring environment variables, and using just 20 lines of code to build, debug, and deploy multi‑agent applications with features like RAG, tool integration, and distributed execution.
This article explains the fundamentals of large language models, covering transformer self‑attention, prompt engineering, API usage with temperature and tool parameters, function calling, agent architectures, the Model Context Protocol (MCP), Agent‑to‑Agent (A2A) communication, and future AI programming roles.
This article introduces the AI‑powered mind‑map‑MCP tool, explains its features, installation via npm or npx, usage through Coze API and Trae configuration, showcases generated mind‑maps, and outlines future enhancements for better NLP understanding and broader integrations.
This guide walks through a real‑world AccessKey security incident, showing how to configure the Alibaba Cloud Observability MCP server, import JSON settings into Cherry Studio, and use natural‑language queries to audit logs, identify risky operations, monitor root account usage, and summarize overall system activity.
The Model Context Protocol (MCP) is an open‑source interface that standardizes how large language models interact with external data sources and tools, offering a USB‑C‑like universal connector for AI applications, with built‑in session management, security, and flexible HTTP/SSE transport for seamless real‑world integration.
This tutorial walks through the complete process of connecting LangChain to a Model Context Protocol (MCP) server, covering environment setup, configuration files, client code, and a PlayWright‑based browser automation example that demonstrates building and running an AI agent with LangChain.
This article explains how the Model Context Protocol (MCP) solves the AI integration "M×N" problem by providing a universal language for AI applications and tools, outlines its technical principles, showcases practical case studies, and promotes the accompanying book that teaches MCP from theory to implementation.
After four months of using the AI code assistant Cursor, the author shares a practical methodology that combines clear requirement articulation, pre‑planning, custom rules, and various Model Context Protocol (MCP) tools—such as feedback‑enhanced, sequential‑thinking, and better‑tapd—to streamline development, reduce token waste, and improve collaboration.
This guide walks developers through Dify's native MCP integration—explaining the protocol, showing step‑by‑step configuration, demonstrating usage in agents and chatflows, and presenting real‑world cases like data analysis and travel planning—to accelerate AI application development.
This article explores how a new AI‑powered workflow combines visual layout detection, design‑system standards, DSL recall, and a custom design‑plugin to turn complex transaction‑page mockups into clean, rpx‑compatible front‑end code in just a few steps, dramatically reducing developer effort and error.
This article explains why the Model Context Protocol (MCP) is needed for LLMs, describes its client‑server architecture, data and transport layers, and provides step‑by‑step Python examples for creating both an MCP server and a client using FastMCP and low‑level APIs.
This article describes how a development team integrated a suite of AI‑driven workflows—including AI‑generated documentation, code scaffolding, rule‑based IDE guidance, MCP server connectivity, and memory banks—to reduce repetitive tasks, accelerate coding, improve code review, and streamline operations across the entire software lifecycle.
This guide walks you through creating a lightweight Spring Boot MCP service that securely exposes CRM data as AI‑callable tools, covering project setup, data model definition, tool implementation, registration, and both STDIO and SSE client connections for seamless AI integration.
This article explains the challenges AI agents face when using numerous MCP servers, introduces Nacos MCP Router as a smart, secure, and token‑efficient solution, details its architecture, smart‑routing and proxy modes, and provides step‑by‑step deployment instructions on a cloud‑native platform.
This article explains how to design and implement a comprehensive, cloud‑native observability framework for AI applications, covering architecture layers, key metrics such as token usage, TTFT and TPOT, OpenTelemetry tracing, Dify deployment tips, model evaluation, and MCP token‑blackhole challenges.
This article analyzes the prompt‑inflation bottleneck that arises when large language models (LLMs) must handle thousands of Model Context Protocol (MCP) services, and introduces the MCP‑RAG architecture—a retrieval‑augmented generation solution that builds a metadata knowledge base and intelligent retrieval layer to enable precise, efficient MCP service discovery at scale.
The article explains how the MCP protocol standardizes AI model interactions with external services, outlines the challenges of deploying MCP servers in enterprises, and demonstrates how Huawei Cloud FunctionGraph's serverless workflow offers a low‑cost, scalable, and secure solution with step‑by‑step deployment guidance.
This article introduces the Model Context Protocol (MCP) as an open standard for connecting large language models to external data and tools, outlines its core advantages, showcases a wide range of ready‑made services, and provides a detailed Python implementation that integrates Sentry bug‑tracking and APM data into AI assistants for real‑time analysis and reporting.
This article explains how Volcanic Engine safeguards the Model Context Protocol (MCP) throughout its lifecycle, detailing MCP fundamentals, core components, a step‑by‑step interaction example, seven major security risks, official design principles, and a comprehensive security architecture covering admission control, native design, and runtime protection.
This article introduces three emerging standards—MCP, A2A, and AG‑UI—that simplify connecting large language models to external tools, other agents, and user interfaces, explaining their origins, architectures, development workflows, key features, and how they complement each other in AI application development.
This comprehensive guide walks you through everything you need to know about Claude Code—from installation on macOS, Linux, and Windows via WSL, to core commands, configuration, MCP integration, security settings, advanced features, and CI/CD automation—ensuring you can harness the full power of this AI-powered CLI tool.
This article explains how the Model Context Protocol (MCP) extends the Cursor AI‑native IDE beyond its built‑in code assistance, enabling real‑time web fetching, memory, automation, design integration, and multi‑model orchestration to create a truly omnipotent AI development companion.
This article provides a concise overview of the latest AI concepts—including AIGC, Retrieval‑Augmented Generation, Function‑Calling models, intelligent agents, and the Model Context Protocol—explaining their principles, differences, and how they can be combined to build more powerful AI applications for developers outside the AI field.
This guide explains three ways Dify can invoke Java programs—using HTTP requests, defining custom tools with OpenAPI schemas, and configuring MCP communication—providing step‑by‑step setup, code examples, and configuration snippets for each approach.
This article explains the three foundational protocols—MCP for tool access, A2A for inter‑agent communication, and AG‑UI for Agent‑UI interaction—detailing their origins, technical roles, example implementations, and how they together form the communication backbone of modern AI applications.
The article explains how the rapidly emerging MCP protocol has become a de‑facto standard for AI agents, why developers should treat it like HTTP, and offers practical learning paths—including official docs, books, and hands‑on projects—to master MCP efficiently.
This article provides a concise overview of the latest AI breakthroughs—including AIGC, multimodal technology, Retrieval‑Augmented Generation (RAG), intelligent agents with function‑calling models, and the Model Context Protocol (MCP)—explaining their principles, relationships, and practical implications for developers outside the AI field.
This article provides a concise, developer‑focused overview of emerging AI concepts—including AIGC, multimodal models, Retrieval‑Augmented Generation, intelligent agents, Function‑Calling, and the Model Context Protocol (MCP)—explaining their core principles, differences, and how they interrelate to enable advanced AI applications.
PaddleOCR 3.1 introduces three major upgrades—a multilingual PP‑OCRv5 model supporting 37 languages with over 30% accuracy gain, a PP‑DocTranslation pipeline for high‑quality multi‑language document translation, and MCP server support for flexible AI application integration—accompanied by detailed CLI usage, demo scenarios, and open‑source resources.
WeChat recently opened its WeChat Pay MCP, a Model Context Protocol that lets AI agents seamlessly accept payments, and this article walks through setting up the integration, showcases a health‑meal demo, discusses potential misuse scenarios, and warns of emerging security risks.
This article walks you through the entire process of enabling and integrating WeChat Pay MCP into Tencent Yuanqi AI agents, covering its core capabilities, trial activation, template usage, code configuration, workflow setup, and practical tips for seamless payment experiences.
This article documents a step‑by‑step investigation of an AI Agent that fails to return results when it selects multiple MCP services, detailing log analysis, network packet capture, MCP protocol behavior, the discovery of tool‑name conflicts, and both temporary and permanent remediation strategies.
This article explains how the open‑source JManus framework enables Java developers to create, configure, and deploy multi‑agent AI applications on Alibaba Cloud's Serverless SAE and Function Compute platforms, highlighting its OpenManus compatibility, MCP protocol support, PLAN‑ACT mode, high‑availability architecture, performance advantages over low‑code solutions, and step‑by‑step deployment instructions.
This article explains the MCP framework that standardizes AI tool invocation using JSON‑RPC, outlines its client‑server architecture, details communication methods such as STDIO, SSE and streamable HTTP, and provides a Spring AI demo showing tool registration, discovery, and execution.
This talk explains the Model Context Protocol (MCP), how it simplifies agent tool integration by replacing numerous custom interfaces with a single standardized protocol, and details its adoption, architecture, security, and future directions within Ant Group's ecosystem.
This article examines the rise of terminal AI coding assistants, comparing Google’s Gemini CLI and Anthropic’s Claude Code across features, context length, cost, performance, platform support, and usage tips, helping developers decide which tool best fits their productivity and project needs.
This article explains the Model Context Protocol (MCP), compares it with traditional function calling, and provides a step‑by‑step guide for integrating MCP into the Cursor editor, including token generation, configuration, server setup, and practical examples that dramatically improve AI‑assisted development productivity.
Model Context Protocol (MCP), an open-source standard from Anthropic, standardizes how large language models interact with external tools and data sources, introducing a client‑server architecture with hosts, clients, and servers, and promises to simplify AI application development compared to traditional function‑calling approaches.
Midscene MCP provides a standardized Model Context Protocol that lets AI models interact with browsers, offering tools for navigation, tab management, UI interaction, verification, and reporting, along with setup instructions, configuration examples, and FAQs to help developers automate web tasks efficiently.
Learn how to configure Figma's Dev Mode MCP, connect it to AI coding assistants like Cursor, and generate accurate mobile front‑end code with Taro and React, leveraging component and icon libraries to streamline design‑to‑code workflows and improve development efficiency.
This article introduces three emerging open‑source MCP projects—FastAPI‑MCP for converting FastAPI endpoints, ACI.dev as a one‑click MCP orchestrator with 600+ tool connectors, and Dive, a cross‑platform desktop client—detailing their purpose, key features, usage steps, and GitHub repositories.
The article explains how the Model Context Protocol (MCP) is shifting from Server‑Sent Events to a single‑endpoint, bidirectional Streamable HTTP transport, detailing the limitations of SSE, the benefits of the new approach, compatibility strategies, and upcoming features for developers.
This guide explains how to overcome Dify's limitations by using Nacos 3.0 to register MCP servers, dynamically manage prompts, and centralize environment variables, enabling rapid AI application iteration without redeployment.
This article walks you through what Spring AI Alibaba 1.0 offers, highlights its major updates such as the Graph multi‑agent framework and ecosystem integrations, and provides a step‑by‑step upgrade path with Maven dependency changes, code fixes, and configuration adjustments for Java developers.
This article introduces the Model Context Protocol (MCP), explains its core advantages such as capability expansion, standardized interfaces, and low‑code integration, and demonstrates a practical Sentry‑MCP implementation with Python code, detailed functions, and visual results for AI‑driven data access.
This article explains the MCP framework’s architecture, execution flow, JSON‑RPC communication, and lifecycle, showing how it standardizes AI function calling and tool integration using Spring AI, with code examples and comparisons of communication methods.
This article explains how to combine Alibaba Cloud's MCP Server with large language models to enable natural‑language operations on cloud products, covering setup, tool selection, OAuth authentication, code examples, troubleshooting context‑length limits, and future enhancements for more efficient, secure cloud management.
The Model Context Protocol (MCP) is an open, standardized adapter that lets large language models seamlessly connect to tools, data sources, and workflows, offering plug‑and‑play intelligence, cross‑platform compatibility, security, and modular extensibility for building real‑world AI applications.
This article examines Nacos 3.0’s architectural evolution from Nacos 2.0, highlighting performance and extensibility upgrades, the new AI Registry with model, tool, and application layers, zero‑trust security enhancements, MCP Registry capabilities, and the roadmap toward a comprehensive AI‑native cloud‑native platform.
The article explains the Model Context Protocol (MCP) as a zero‑code bridge that lets large language models seamlessly access databases, external APIs, and execute code, detailing its benefits for developers and everyday users, its core components, step‑by‑step workflow, real‑world examples, and how it outperforms traditional APIs in modern AI agent systems.
