The article explains how to replace monolithic prompts with reusable, lazy‑loaded Skill files, compares Skills with Prompt, MCP and Function Calling, shows concrete Skill structures and examples, and demonstrates a Spring Boot AI interview platform with open‑source repositories.
The gws CLI bundles every Google Workspace API into a single command‑line tool, offering dynamic command generation, structured JSON output, and native MCP support for AI agents, with easy npm installation, step‑by‑step usage examples, OpenClaw integration, and essential tips to avoid common pitfalls.
Claude Code 2.1.69 introduces a lazy‑loaded Tool Search that defers loading of Model Context Protocol (MCP) tools until needed, dropping system‑tool context from around 10% to zero, dramatically reducing token consumption and improving tool selection accuracy.
This guide walks you through eight production‑grade OpenClaw scenarios—including cron‑driven briefings, webhook integrations, MCP data access, multi‑agent roles, browser automation, mobile camera monitoring, custom skill creation, and workspace configuration—providing complete configuration files and command‑line examples for immediate use.
This article explains the fundamental differences between web session tokens and AI tokens, compares MCP and Skill token consumption, presents pricing formulas for major models, and offers practical strategies—including prompt compression, context management, and dynamic toolsets—to dramatically reduce AI token expenses.
QueryWeaver, an open‑source Text‑to‑SQL solution from FalkorDB, leverages a graph database to model schema relationships, offering instant natural‑language querying, MCP integration for AI IDEs, and a detailed comparison with Wren AI highlighting its fast, out‑of‑the‑box experience.
This article walks through five core configuration areas—identity via CLAUDE.md, MCP tool integration, Hooks automation, reusable Skills, and fine‑grained permission control—showing how to turn Claude Code from merely usable into a highly efficient, secure AI coding assistant.
This guide explains how to combine Figma, Cursor, and the Model Context Protocol (MCP) to create realistic, interactive front‑end prototypes that match design files, reduce hand‑off ambiguity, and generate near‑production code without replacing the engineering process.
This article clarifies the distinct roles of Agent Skills and Model Context Protocol (MCP), compares their core features, shows how they complement each other through design philosophy and real‑world scenarios, and provides a decision framework for choosing the right tool in AI agent architectures.
This article explains the core concepts of Function Calling, Model Context Protocol (MCP), and Skills, showing how they build on each other to enable reliable tool use, reduce integration costs, and let users define complex workflows with natural language.
This guide walks through installing the MCP MySQL server npm package, using its five command‑line tools, testing with the Inspector client, configuring the Cursor environment via JSON, restarting Cursor, and executing basic SQL commands through the @mysql‑local interface.
This step‑by‑step guide shows how to activate the McDonald’s Model Context Protocol (MCP), integrate it into Cherry Studio, install Claude Code support, and use AI commands to automatically retrieve and claim McDonald’s coupons.
Learn how to connect PHP-based AI agents to the Model Context Protocol (MCP) using the open‑source Neuron AI framework, covering MCP fundamentals, server setup, tool integration, and example code for creating custom agents that can invoke external APIs, databases, and web content.
This tutorial shows how to extend the MiniManus agent framework with Skill support, explains why Skills are needed compared to plain MCP, details the Claude Skill specification, provides concrete command‑line operations, code implementations, and demonstrates Skill‑MCP collaboration through practical examples.
This article walks through the complete development of a minimal Model Context Protocol (MCP) server using Spring Boot 3 and Server‑Sent Events, illustrating how OpenSpec drives the workflow from proposal and task breakdown to design, implementation, testing, and integration with Claude Code.
This tutorial explains how the Model Context Protocol (MCP) standardizes AI agent integration by replacing custom tool code with a JSON‑RPC based, auto‑discovered ecosystem, walks through configuration, core loading logic, code implementation, a runnable example, and compares MCP with traditional tool use.
This article breaks down Anthropic’s 33‑page guide on building Claude Skills, explaining the two‑layer MCP/Skill architecture, progressive disclosure design, file‑naming rules, reusable patterns, testing methods, and distribution options for creating stable, reusable AI workflows.
This article provides a detailed technical guide on Anthropic's Claude Skills, explaining their definition, file structure, progressive disclosure design, real‑world use cases, step‑by‑step implementation instructions, core design patterns, testing methods, success metrics, and iteration signals for building robust AI agents.
The article argues that, for AI agents in 2026, lightweight CLI tools accessed via the terminal outperform the heavyweight Model Context Protocol (MCP) by leveraging existing shell utilities, reducing token overhead, and preserving Unix composability, ultimately delivering faster, cleaner development workflows.
Draw.io (now diagrams.net) has released an official MCP server that lets AI‑enabled editors like Cursor generate and edit diagrams instantly by converting AI‑produced Mermaid, CSV, or XML data into a Draw.io link, offering a seamless, editable workflow with privacy‑preserving URL‑based rendering, though limited by URL length.
This article synthesizes Anthropic’s recent Claude Agent blogs, presenting a layered architecture and practical steps to transform chat‑centric agents into reliable, production‑ready systems, covering when to adopt multi‑agent setups, the role of Skills and MCP, and a ready‑to‑use implementation checklist.
This article explains the complementary roles of MCP and Skill for Claude, compares them with real‑world analogies, outlines typical use cases, lists advantages and disadvantages, and provides decision guidance on when to use each or combine them.
In this interview, veteran iOS developer Peter Steinberger explains how his open‑source project Clawdbot (now Moltbot) evolved from a personal need for an autonomous assistant, detailing its rapid GitHub growth, WhatsApp integration, CLI‑first philosophy, security considerations, and his vision for a future where personal AI agents replace traditional apps.
This article explains how to maximize the efficiency of Claude Code by understanding context windows, selecting optimal tokens, using commands like /upgrade, /model, and /init, and leveraging subagents, MCP servers, and skills to reduce token consumption while maintaining high‑quality AI responses.
This article breaks down the core concepts from the free Anthropic short course—Tools, Skills, the Model Context Protocol (MCP), and Subagents—explaining their roles, differences, and how they combine to build reliable, parallelizable AI agents, illustrated with a customer‑insight case study.
The article examines the rise and fall of MCP, explains why simply adding AI‑driven Skills without rethinking workflows leads to “skill debt,” and outlines three hidden costs—expert knowledge capture, version drift, and context explosion—while proposing practical solutions such as experience capture, Skill‑as‑Code testing, and dynamic routing.
This article systematically explains the concepts of MCP, Agent, Skill, and Rule from an engineering viewpoint, highlighting their roles, differences from traditional API calls, and how they enable large language models to safely and autonomously interact with external tools.
This guide explains how to treat Claude Code as a long‑term partner by configuring lightweight fine‑tuning, organizing reusable skills and slash commands, leveraging event‑driven hooks, deploying focused subagents, managing Model Context Protocol (MCP) and plugins, and applying productivity tips to maximize AI‑assisted coding efficiency.
This article explains how AI Skills serve as structured knowledge bases that complement, rather than replace, Model Context Protocols, enhance Retrieval‑Augmented Generation, and drive three major trends—standardized agent stacks, low‑code knowledge engineering, and the emergence of personal AI agents.
This article introduces a TypeScript MCP‑Server framework that simultaneously supports stdio, streamableHttp, and SSE protocols, explains its design motivations, outlines its modular architecture, provides setup and deployment instructions, and showcases key source code and usage examples.
This article introduces a TypeScript MCP‑Server framework that simultaneously supports stdio, StreamableHttp, and SSE protocols, explains its motivations, core features, directory layout, startup commands, production configuration, key code snippets, and showcases deployment results.
Everything Claude Code, an open‑source, production‑tested Claude AI coding assistant, rapidly gained 25.7k stars by offering a modular, engineering‑grade configuration that standardizes code style, automates rule enforcement, and integrates hooks, agents, skills, commands, and MCP to streamline AI‑driven development.
This article explains the three‑layer Agent Skills architecture, demonstrates step‑by‑step creation and configuration of a Skill using Claude Code—including metadata, instruction, and resource layers, advanced scripting integration, and a detailed comparison with MCP, highlighting token savings and use‑case differences.
Anthropic’s technical blog explains the shift from building domain‑specific agents to creating reusable Agent Skills, detailing the new code‑centric paradigm, progressive disclosure, skill ecosystem, real‑world examples in finance and healthcare, and the emerging architecture that unifies agents, runtimes, MCP servers, and skill libraries.
Anthropic’s recent blog introduces a four‑layer agent architecture—Loop, Runtime, MCP, and Skills—showing how treating code as a universal interface enables agents to execute real‑world tasks, while Skills package reusable, versioned workflows, scripts, and documentation to bridge the gap between model intelligence and practical operations.
This guide shows how to transform Claude Code from a personal shortcut into a stable, version‑controlled team asset by organizing its usage into seven configurable components, outlining a step‑by‑step rollout, and providing practical examples, repository structures, plugins, and common pitfalls.
The article argues that the recent hype around Skills and the criticism of MCP stem from a mindset problem: developers treat MCP like a REST API wrapper, leading to poor server designs, and it offers six concrete design principles to build agent‑friendly MCP services.
Anthropic has released the Claude Code VS Code plugin, adding a Spark icon that opens an AI‑powered coding assistant directly in the editor, with features like @‑based fuzzy file referencing, WYSIWYG context capture, Plan Mode approvals, Inline Diffs, Git and MCP integration, and terminal support, while still retaining CLI‑only checkpoints for rollback.
This guide walks through designing and implementing an AI assistant that connects FastAPI to Alibaba Cloud large‑model services, supports streaming text Q&A, image understanding, text‑to‑image generation, network search, and MCP‑based map queries, with full front‑end and back‑end code examples.
The article explains how mcpx reduces MCP tool definition tokens from tens of thousands to a few hundred by discovering tools at execution time, improving accuracy and speed for local large language models while preserving prompt cache integrity.
This article explains why a unified MCP‑Server framework supporting stdio, streamableHttp, and SSE is needed for AI‑driven services, describes its modular design, directory layout, startup commands, environment‑based configuration, key code components, deployment scripts, and showcases successful integration with JoyCode and JoyAgent.
The article explains the rapidly expanding AI Agent protocol stack, clarifies that protocols form layered, non‑competing standards such as MCP, A2A, AG‑UI, A2UI, AP2 and UCP, and offers practical guidance on when and how to adopt each layer in real‑world scenarios.
This article provides a detailed, hands‑on tutorial for creating an AI agent, covering registration and API key setup for Alibaba Cloud, OpenAI, Dashscope and DeepSeek, installing and using Ollama for local model deployment, configuring CherryStudio, and implementing function‑calling and MCP techniques with full code examples.
The article analyzes Anthropic's new Agent Skills concept, explaining how it addresses the token‑bloat and positioning gaps of MCP, outlines its progressive‑disclosure design, shows a file‑system structure, and discusses practical usage and early platforms for skill sharing.
Claude Code introduces MCP Tool Search, a lazy‑loading mechanism that dynamically loads only needed tools, cutting token usage by over 67,000 tokens in large MCP setups, preventing context bloat, improving performance, and offering developers regex and BM25 search options with defer_loading support.
This article walks you through the entire process of creating Claude Code plugins—from understanding why plugins are needed, setting up the directory structure, writing commands, agents, skills, and hooks, to testing, debugging, advanced features, and publishing—providing concrete code examples, configuration files, and best‑practice recommendations.
TuriX-CUA is an open‑source Python framework that lets AI agents understand screen content and perform mouse‑keyboard actions on Windows and macOS, offering high success rates, model hot‑swapping, MCP integration, and step‑by‑step installation for desktop automation tasks.
This article details the design and implementation of a lightweight, non‑intrusive data‑collection framework for multiple AI coding tools—covering background challenges, industry solutions, design principles, a three‑layer MCP‑based architecture, concrete hook scripts, telemetry handling, cross‑platform deployment, and future enhancements.
This article evaluates the flood of Model Context Protocol (MCP) servers that have emerged since Anthropic released the protocol, defines clear criteria for selecting useful servers, and provides detailed installation steps, real‑world usage examples, and security best practices for six essential MCP servers that boost developer productivity.
This guide introduces AgentScope, a multi‑agent framework for Java that brings ReAct reasoning, tool calling, memory management, RAG, and serverless capabilities to LLM‑powered applications, and provides step‑by‑step code examples for basic and advanced usage.
This article analytically compares Skills (brain‑level expertise) and MCP (external tool interface) for building AI agents, detailing their architectures, development effort, scalability, security, and real‑world use cases, and provides a decision framework to help engineers select the optimal combination.
Claude Code Workflow Studio is a VSCode extension that lets you visually design, export, and run AI‑driven automation workflows without writing code, offering low cognitive load, AI‑assisted editing, Slack sharing, and Git‑friendly version control for test and data processing scenarios.
The article explains why traditional chat‑based AI tools are limited to advice, introduces next‑generation LLM‑native applications that can understand, plan, and act, and provides a step‑by‑step guide on designing AI workflows, autonomous agents, hybrid architectures, and the Model Context Protocol (MCP) using LangChain.
This guide explains why traditional Retrieval‑Augmented Generation (RAG) struggles with precision, introduces the Model Context Protocol (MCP) as a standardized way for large language models to interact with external data sources, and provides step‑by‑step instructions for integrating MCP with MongoDB using Cherry Studio and VSCode +Cline.
This article provides a detailed, source‑code‑level analysis of Spring AI’s Model Context Protocol (MCP) implementation, covering its layered architecture, core client‑server interaction steps, key components, runtime debugging with SSE, and practical code examples for building and testing MCP services in Java.
This article explains the fundamental differences between Model Context Protocol (MCP) and Agent Skills, covering their design philosophy, technical architecture, token costs, security considerations, practical use‑cases, and how they complement each other in building powerful AI agents.
This article provides a comprehensive analysis of Spring AI’s Model Context Protocol (MCP), covering its layered client‑server architecture, core interaction sequences, key source‑code components, and step‑by‑step debugging of the SSE‑based initialization flow, enabling developers to integrate AI capabilities into Java applications with confidence.
This article explains how Anthropic's Claude Skill folders let you package domain expertise, scripts, and resources into reusable modules, differentiate Skills from prompts, combine them with MCP tools and workflows, and build a robust mixed Agent‑Workflow architecture for reliable enterprise automation.
This article explains GitHub's AI ecosystem—Models, Copilot, Agents, Spaces, Spark, Instructions, Skills, and the Model Context Protocol—clarifying each component, their relationships, and practical steps for developers to integrate them into their workflow.
Z Code, a lightweight AI code editor now in Alpha for Mac and Windows, offers a visual GUI that bundles top agents Claude Code, Codex, and Gemini, enables single‑API‑key switching, provides granular permission modes, conversation‑based versioning, and built‑in Model Context Protocol support for external data sources.
This article explains how to reduce token consumption and improve AI‑driven code repairs by converting Sonar scan results into structured prompts using the Model Context Protocol (MCP) and a six‑step intelligent prompt generation pipeline integrated with Cursor.
This article details how a front‑end team generated high‑quality React code from only UI screenshots by iteratively applying imgcook, an Agent‑driven MCP service, and custom prompts, achieving up to 80% code usability and demonstrating a repeatable script‑based workflow.
This article explains how the cloud‑native Knative serverless framework reduces GPU waste, enables request‑driven autoscaling to zero, accelerates AI model versioning and startup with Fluid, and integrates protocols like MCP and A2A to deliver cost‑effective, high‑performance AI inference services.
This article explains how Anthropic's Skills and Model Context Protocol (MCP) servers complement each other to let Claude agents follow specific workflows, access external tools, and produce consistent, reliable outputs, illustrated with real‑world use cases and a quick reference guide.
This article explains how the Model Context Protocol (MCP) enables controllable, auditable automation of Android development tasks—bridging large language models with local tools like Gradle, ADB, and emulators—to improve efficiency, safety, and workflow integration.
This article demystifies Claude's Agent Skills by defining their structure, comparing them with Rules and MCP, illustrating progressive disclosure with concrete examples, and providing step‑by‑step installation and usage instructions for both official and custom skills.
This article explains how next‑generation AI agents, combined with OpenSearch and the Model Context Protocol (MCP), enable semantic, multimodal, conversational, and agentic search, detailing the architecture, deployment options, toolset, and practical demos for sales analytics and DevOps use cases.
The report analyzes the rapid emergence of Claude Skills and OpenAI's Agentic Commerce Protocol, detailing their technical architectures, benchmark performance, cross‑platform compatibility, enterprise adoption metrics, security challenges, and strategic implications for businesses entering the agentic AI era.
Claude’s latest update open‑sources the Skills feature as a universal standard, replacing the cumbersome Model Context Protocol (MCP) with installable “skill packages” that run on any platform—Claude web, CLI, Cursor, VS Code, and other AI agents—effectively creating an AI‑focused app store.
The article introduces the Model Context Protocol (MCP), an open Anthropic standard that creates a secure, standardized, bidirectional bridge between large language models and external tools, then walks through its architecture, core components, Python server and client code, OpenAI integration, usage flow, ecosystem and future outlook.
This guide explains the concept of human‑in‑the‑loop (HITL) interruptions in LangGraph, outlines the core mechanisms such as persistent state and dynamic/static interrupts, and provides detailed Python examples for four classic patterns—approval/rejection, state editing, tool‑call review, and input validation—plus advanced topics like parallel interrupts and MCP‑based tool integration.
Claude Skills are modular, file‑system‑based knowledge packages that run in a sandbox VM, enabling agents to inject deterministic SOPs through progressive disclosure, with metadata always loaded, core instructions loaded on demand, and code/resources fetched only when needed.
This article explains the origins of AI Agent Skills, walks through the standard tool‑use loop, provides step‑by‑step code for defining and invoking Skills with the OpenAI API, compares Skills with the emerging Model Context Protocol (MCP), and offers practical guidance for developers and architects building AI‑enabled systems.
This article details how we built an AI‑tool ecosystem by evolving a basic online JS cloud‑function platform into a unified, reusable capability layer that integrates with Flowise, LangChain StructuredTool, and the Model Context Protocol (MCP) to provide secure, cross‑platform tool calls for agents.
This guide walks you through installing the Harvester MCP server on Windows, configuring it with a kubeconfig, and integrating it into Cursor's MCP settings so you can query and generate Kubernetes commands directly from your editor.
This article explains how to design and implement Human‑in‑the‑Loop (HITL) interactions for large‑model agents on Alibaba's OpenLM platform, covering the challenges of server‑side execution, MCP transport extensions, tool‑calling patterns, timeout handling, and UI rendering strategies across multiple client devices.
This article provides a comprehensive technical overview of the Model Context Protocol (MCP), an open‑standard JSON‑RPC 2.0 protocol that enables large language models to securely interact with external data sources, tools, and services, detailing its design, architecture, Python SDK implementation, transport mechanisms, and real‑world deployment examples such as the DW‑DBA‑MCP project.
This article explains how the open‑source Skill Seekers tool automates the extraction, cleaning, structuring, and packaging of documentation, code repositories, and PDFs into Claude‑compatible Skills, enabling rapid onboarding of obscure frameworks while highlighting conflict detection, MCP integration, and practical usage tips.
This article breaks down Anthropic’s latest Agent engineering concepts—MCP, Programmatic Tool Calling (PTC), Skills, and Subagents—showing how they work together to reduce latency, token cost, and context overload while enabling modular, scalable AI workflows.
This article explains the MCP protocol for AI agents, detailing why a universal tool‑calling standard is needed, how it solves the M×N integration nightmare, the roles and execution stages involved, and demonstrates its use with Cherry Studio while highlighting current limitations.
This article walks through the design and implementation of an AI‑driven Jira assistant that uses LangGraph as the agent brain, Retrieval‑Augmented Generation for knowledge access, and a Model Context Protocol (MCP) server to execute Jira operations, complete with architecture diagrams, code snippets, and practical use cases.
This article details a multi‑agent AIOps solution built on the Dify platform that automates fault detection, root‑cause analysis, and incident reporting by integrating metrics, logs, and trace data, dramatically reducing mean time to detect and resolve complex cloud‑native service failures.
Celebrating one year since its debut, Anthropic’s Model Context Protocol (MCP) releases a major update that introduces task‑based workflows, URL‑based OAuth client registration, enhanced security requirements, extensible components, and a suite of new features aimed at improving reliability, observability, and developer flexibility.
This article explains the fundamentals of AI‑generated content (AIGC), the Retrieval‑Augmented Generation (RAG) technique, Function Calling, autonomous agents, and the Model Context Protocol (MCP), highlighting their evolution, technical workflows, limitations, and real‑world examples for developers.
Marking its one‑year anniversary, the Model Context Protocol (MCP) receives a critical update that adds experimental task primitives, a new client‑metadata registration, security hardening, and sampling tools, while the competing A2A protocol offers built‑in multi‑agent support, stronger security by default, and broader industry backing, highlighting a clear shift in agent communication standards.
This guide details a private MCP gateway architecture using open‑source Higress as the MCP proxy and Nacos as the registry, enabling dynamic tool registration, real‑time Prompt updates, multi‑tenant isolation, and deployment on an air‑gapped Kubernetes cluster without Helm.
After spending $47,000 running four LangChain agents in production, we reveal the hidden costs of A2A communication and Anthropic’s MCP, expose seven common deployment pitfalls, and argue that dedicated AI infrastructure is essential for scalable multi‑agent systems.
This article details how, starting from only UI screenshots and no design files, a team used imgcook, an Aone Agent with MCP service, and prompt engineering to automatically generate mobile front‑end code, iteratively improving usability from 40% to 80% and documenting the full workflow.
The article explains how Easy Code Reader MCP Server integrates with Joycode and large language models to automatically retrieve and analyze Java source code across multiple projects or JAR files, improving code‑agent accuracy and developer productivity.
This article explains how the Easy Code Reader MCP server integrates with large language models to automatically locate, read, and analyze Java source code across multiple micro‑services and JAR files, improving code‑agent accuracy and developer productivity through a set of practical tools and step‑by‑step examples.
Spring AI 1.1.0 arrives with a major overhaul, adding out‑of‑the‑box Model Context Protocol support, five‑mode prompt caching that can cut LLM costs by up to 90%, reasoning APIs, recursive advisors, a broadened model ecosystem, enhanced vector‑store and chat‑memory options, and richer observability integrations.
This article details how Alipay's engineering team built an AI‑Agent‑powered coding assistant for Kotlin Multiplatform (KMP) native cross‑platform development, covering architecture, UI generation from designs and images, RAG‑based knowledge retrieval, crash analysis, and future directions for AI‑driven software engineering.
This article explores how to create a self‑thinking AI data analyst by detailing agent fundamentals, core modules such as planning, memory and tool scheduling, practical development steps, multi‑agent collaboration, evaluation benchmarks, and real‑world examples like stock backtesting.
This article walks through setting up MCP servers in both stdio and SSE modes, shows how to load their tools into LangChain agents using the new create_agent API, compares the two communication methods, and provides complete runnable Python examples.
The article introduces the Jupyter MCP server, an extension that implements the Model Context Protocol to enable seamless, secure integration of large language models with Jupyter’s kernels, file system, and terminals, and details its architecture, core features, deployment steps, API mechanisms, and security considerations.
This guide dives into the four advanced capabilities of LangChain 1.0's create_agent API—MCP tool integration, structured output, memory management, and middleware—showcasing practical examples such as an Amap MCP planner, Pydantic‑based response formatting, InMemorySaver chat history, and custom middleware for dynamic model selection.
This article explores the evolution of security alert automation from manual verification to SOAR and AI-driven solutions, detailing MCP-based AI agents, integration with various security tools, practical case studies of honey‑pot, HIDS, and EDR alert tracing, and the resulting efficiency gains and future outlook.
This article introduces five free, open‑source Model Context Protocol (MCP) services that extend AI capabilities—covering web scraping, browser automation, document‑to‑Markdown conversion, real‑time documentation, and structured reasoning—along with concise step‑by‑step setup instructions for each.
This article introduces eight core MCP components—Grafana, Jenkins, K8s, Playwright, GitHub, Zabbix, Prometheus, and Alibaba Cloud—detailing how each enhances monitoring, automation, resource management, and performance optimization, thereby dramatically improving operational efficiency and reducing manual workload.
Facing the growing complexity of big‑data platforms, the SRE team adopted large‑language‑model agents to automate knowledge management and root‑cause analysis, employing Retrieval‑Augmented Generation, a vector store, and the Model Context Protocol to enable intelligent, scalable, and efficient incident diagnosis and resolution.
