MCP, the Model Context Protocol released by Anthropic and now an open standard, provides a universal USB‑C‑like interface that lets AI tools connect to external services through a single set of adapters, reducing integration complexity from O(N×M) to O(N+M) and enabling scalable, plug‑and‑play AI capabilities.
The article analyzes why connecting an MCP server can triple response latency and token usage, explains how unstable tool ordering breaks Anthropic's prompt cache, and provides detailed code walkthroughs, design insights, common pitfalls, and concrete best‑practice recommendations for building reliable MCP integrations.
The Model Context Protocol (MCP) embeds every tool’s description into an LLM’s context window, creating a structural “Context Poisoning” vulnerability that lets malicious or bloated tool metadata hijack agent reasoning, inflate tokens, and bypass traditional input validation.
The 2026 Agent Skills whitepaper, officially released by the算泥 community, analyzes the evolution of prompting, function calling, the Model Context Protocol (MCP), core limitations of current AI agents, and the rapid expansion of the Agent Skills ecosystem across coding and non‑coding scenarios.
In early 2026 the AI community witnessed a sharp shift away from Model Context Protocol (MCP) toward CLI‑first approaches, driven by token‑cost inflation, fragmented authentication, and loss of composability, with developers favoring the lightweight, text‑based nature of command‑line tools for building robust agent pipelines.
The article analyzes how AI has shifted from a code‑completion add‑on to an integral part of software engineering, highlighting the rise of intelligent agents, the Model Context Protocol standard, trust concerns, the move from "vibe coding" to verification‑first practices, and the evolving skill set required of developers and teams.
The article traces the evolution from early function calls to Tool Calling 2.0, explaining how Model Context Protocol standardizes tool interfaces, how Agent‑to‑Agent communication enables multi‑AI collaboration, and how Skills combine tool orchestration into autonomous workflows, while also discussing remaining challenges such as intent misinterpretation and security risks.
The article explains MCP (Model Context Protocol) as a standardized interface for AI applications to access external data sources and tools, compares it with function calling, outlines its security considerations, architecture, resource types, and transport options such as Stdio and Streamable HTTP.
Although the Model Context Protocol (MCP) was launched by Anthropic in late 2024 and initially praised, users now report severe context‑window costs, instability, and cumbersome authentication, leading Perplexity and others to abandon it in favor of traditional CLI tools that remain more composable and reliable.
This article breaks down Anthropic's four‑layer AI Agent architecture, explains when multi‑Agent setups are worthwhile, details how to design reusable Skills and a standardized MCP connection protocol, and provides a practical checklist and a ready‑to‑use Skill template for immediate implementation.
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.
Claude’s new Interactive Tools, powered by the Model Context Protocol, embed Slack, Figma, Asana and other SaaS UIs directly in the chat, letting users draft, preview, and send messages or generate FigJam diagrams without leaving the conversation, though the feature is limited to paid plans and raises trust and privacy concerns.
The article breaks down Claude Code’s six building blocks—Subagents, Skills, Model Context Protocol (MCP), Slash Commands, Hooks, and Plugins—detailing their characteristics, execution methods, programmability, complexity, autonomy, use cases, customization difficulty, persistence, and integration patterns, and concludes with a comparative matrix and key distinctions.
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.
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.
MCP (Model Context Protocol) is an open standard that lets AI applications connect to external systems through a unified client‑server model, exposing Tools, Resources, and Prompts, while addressing security, permission, and audit concerns to make large‑model deployments more reusable and controllable.
This article introduces the Model Context Protocol (MCP), a universal standard that lets large language models seamlessly connect to databases, APIs, local files, and third‑party services, explains its architecture, core primitives, practical Python implementation, trade‑offs, security considerations, and how it compares with other integration approaches.
One year after its launch, the Model Context Protocol (MCP) receives a patch-heavy update adding Task abstraction, client-metadata registration, security hardening, and server-side sampling, while the newer A2A protocol offers bidirectional, asynchronous, secure, and extensible multi-agent communication, marking a paradigm shift in agentic AI.
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.
Anthropic has transferred its Model Context Protocol to the Linux Foundation, creating the Agentic AI Foundation with co‑founders Anthropic, Block and OpenAI and backing from Google, Microsoft, AWS and others, turning MCP into a neutral, open‑source standard that lets any AI model plug into data sources like a USB‑C connector.
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.
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.
Block, a fintech giant, deployed AI agents across all 12,000 staff in eight weeks by adopting the Model Context Protocol, simplifying installation, offering model choice, automating tool management, and building a supportive community, revealing key lessons for enterprise AI adoption.
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.
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.
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 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.
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 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.
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.
The article introduces shadcn‑ui‑mcp‑server, a Node.js tool that implements a Model Context Protocol to let AI assistants read shadcn/ui component documentation, source code, and demos, enabling richer code suggestions and full‑page layout generation within editors like VS Code, Continue, and Cursor.
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 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.
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 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.
An in‑depth guide outlines the nine core technologies—ranging from autonomous agent fundamentals and multi‑agent collaboration to workflow orchestration, retrieval‑augmented generation, fine‑tuning, function calling, model context protocols, agent‑to‑agent communication, and AI‑driven UI—required to design, deploy, and scale enterprise‑grade AI agents.
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.
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.
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.
The article introduces Model Context Protocol (MCP), explains its architecture of Host, Client, and Server, describes its components—Resources, Tools, Prompts—and demonstrates practical integration with IDE plugins to extend LLM capabilities such as real‑time ticket queries, highlighting its significance for AI development.
This guide shows how to integrate Spring Boot with the Model Context Protocol (MCP) to let large language models interact with existing services via natural language, covering entity definitions, Maven dependencies, configuration, tool annotations, and testing to create an AI‑enabled book management API.
This article explains how the Model Context Protocol (MCP) standardizes AI agent communication with external tools, outlines its benefits, describes its core components, showcases open‑source implementations, and provides step‑by‑step Python examples for building MCP servers and clients.
This article critically examines the rise of Model Context Protocol (MCP) in AI, explaining its purpose as a unified tool‑calling standard, detailing its architecture, comparing it with traditional function calls, and evaluating the technical and market challenges that limit its universal applicability.
This article explains the Model Context Protocol (MCP), an open standard that lets AI models, tools, and agents share context and communicate through a central server, detailing its definition, key components, workflow, benefits for developers, and real‑world examples.
This article systematically analyzes the security threats of the Model Context Protocol (MCP) ecosystem—including supply‑chain poisoning, backdoors, tool‑description injection, malicious updates, cloud‑service abuse, installer vulnerabilities, and open‑source risks—and proposes concrete governance measures and early‑stage implementations to help enterprises secure MCP deployments and operations.
The Model Context Protocol (MCP) is an open, client‑server protocol introduced by Anthropic that standardizes how applications provide context to large language models, offering a USB‑C‑like interface for tools, data sources, and services to enable reliable, extensible, and secure AI interactions.
This article walks through the Model Context Protocol (MCP) concepts, demonstrates how Cursor leverages Docs and Rules to develop a weather‑service MCP server in TypeScript, explores a gold‑price prediction project, and shares practical tips, advantages, and limitations of AI‑assisted coding.
This article explains the Model Context Protocol (MCP) for AI development, detailing its definition, core components, communication methods, execution process, relationship with agents and function calling, ecosystem growth, and future implications and challenges.
This article explains why current AI models struggle to cooperate, introduces the Model Context Protocol (MCP) as a universal “translation standard” for AI interoperability, and outlines its core features, practical benefits, limitations, and steps developers and users should take to adopt it.
This article introduces the Model Context Protocol (MCP), explains its client‑server architecture, shares practical development experiences and debugging tips for Node.js and Python implementations, discusses common pitfalls such as environment setup, hallucinations, and error handling, and reflects on the broader implications of AI‑driven services.
Anthropic’s Model Context Protocol (MCP) offers a standardized way for AI agents to access and manage external data, tools, and memory, enabling clearer control separation, modular architecture evolution, and scalable enterprise deployments, with a roadmap emphasizing cloud‑native features and advanced agentic workflows.
This guide explains the Model Context Protocol (MCP) that lets AI models securely access files, databases, APIs, and other resources, and presents a curated list of standout MCP servers across categories such as aggregators, web automation, cloud platforms, finance, security, and more, helping developers enhance AI workflows.
The Model Context Protocol (MCP) is emerging as a de‑facto standard for AI interactions, and cloud‑hosted MCP servers are gaining traction by lowering developer barriers, enhancing SaaS integration, meeting enterprise compliance, while also facing deployment speed, traffic variability, and complex logic challenges that serverless function compute aims to resolve.
The article reviews the shortcomings of traditional large language models, explains how function calling extends LLMs beyond pure text, introduces the Model Context Protocol (MCP) as a standardized USB‑C‑like interface for AI tools, and demonstrates a Python MCP example that integrates LLMs with Tencent Advertising APIs.
MCP (Model Context Protocol) is an emerging open‑standard that lets large language models interact with tools, data sources, and services through a unified JSON‑RPC or gRPC interface, addressing fragmentation, data silos, and low development efficiency while offering a flexible, community‑driven ecosystem with clear advantages and current limitations.
This guide walks developers through creating a reusable MCP (Model Context Protocol) tool for Cursor AI—encapsulating the SDK, configuring files, implementing PSD‑slicing logic, defining JSON schemas, adding utilities and build scripts, and deploying it as a command‑type server while avoiding common integration pitfalls.
The article explains Anthropic’s Model Context Protocol, outlines its architecture, and provides a step‑by‑step guide to creating a Spring AI‑based MCP server in Java—including adding the starter, defining @Tool‑annotated services, packaging the jar, configuring the Cline plugin, and demonstrating advanced tools such as Elasticsearch queries.
This article explains how the Model Context Protocol (MCP) decouples tool providers from AI agents, introduces a USB‑C‑like standard for context and tool exchange, and demonstrates its impact on development paradigms, tool ecosystems, and future AI agent architectures.
This article details the design, implementation, and practical usage of a Model Context Protocol (MCP) service for Ant Design components, explaining how to extract component documentation, define MCP tools and prompts, integrate with AI models like Claude, and improve frontend development efficiency.
This guide explains how to use the php-mcp/server library to create a standards‑compliant Model Context Protocol (MCP) server in PHP 8, covering installation, key features, attribute‑based definitions, configuration, transport options, discovery, and code examples for a simple stdio server.
The Model Context Protocol (MCP) is an open, USB‑like standard that lets large language models securely connect to external data sources, tools, and services through a client‑server architecture, enabling developers to integrate diverse resources with standardized SDKs, fostering rapid, scalable AI‑enhanced applications across many domains.
This tutorial explains the Model Context Protocol (MCP) for integrating large language models with external data sources, details its architecture, connection lifecycle, and core JSON‑RPC features, and provides a step‑by‑step Python implementation that fetches real‑time tech news and integrates with Claude Desktop.
This guide explains the Model Context Protocol (MCP), compares six Python and TypeScript AI frameworks that implement MCP, demonstrates their architectures, registries, and code integrations—including OpenAI Agents SDK, Praison AI, LangChain, Chainlit, Agno, and Upsonic—while also discussing the benefits, challenges, and future standardization of MCP in AI agent development.
This article provides a comprehensive overview of the Model Context Protocol (MCP), explaining its origins, architecture, and how it standardises AI tool calling by enabling developers to build MCP servers and clients with TypeScript and Python, register resources, tools and prompts, and orchestrate model‑driven tool execution via JSON‑RPC.
Model Context Protocol (MCP) is an open standard that standardizes how large language models interact with external tools and data, enabling seamless function calls, simplifying prompt engineering, and allowing developers to build modular AI applications without handling low‑level integration details.
This article introduces Anthropic’s open Model Context Protocol (MCP), explaining its basic concepts, motivations, core architecture, components, and workflow, and shows how it standardizes and secures LLM interactions with external data sources, tools, and services.
The article examines how AI agents differ from static workflows, outlines the ideal characteristics for agent tasks, explores codebase indexing, RAG and Function Call techniques, and introduces the Model Context Protocol (MCP) as a standardized, efficient bridge between large language models and enterprise tooling for next‑generation AI‑driven software development.
This article provides a comprehensive overview of the Model Context Protocol (MCP), explaining its purpose in unifying AI tool calls, detailing its architecture, and walking through step‑by‑step development of MCP servers and clients with TypeScript and Python examples, while showcasing real‑world use cases and debugging tips.
This guide walks through converting a traditional Spring Boot book‑management API into a Model Context Protocol (MCP) service that can be invoked via natural‑language prompts, covering dependency setup, proxy configuration, @Tool annotations, MCP server registration, chat client wiring, data initialization, and end‑to‑end testing.
This article explores the Model Context Protocol (MCP), the emerging “USB‑C” for AI, detailing its core advantages, implementation with Kubernetes, a six‑layer cloud‑native architecture, practical code examples, and developer guidelines for building AI‑powered, secure, and scalable services.
The article examines the Model Context Protocol (MCP), detailing its microkernel‑based technical architecture, development timeline from Anthropic’s 2024 release to industry adoption, hands‑on implementation examples, and business use cases such as multi‑agent QQ robots, highlighting MCP’s potential to standardize AI tool integration across industries.
The article analyzes the Model Context Protocol (MCP) as an emerging open standard that unifies how applications provide context to large language models, reviews its rapid ecosystem growth, highlights security and performance challenges, and discusses future directions such as vertical small‑model opportunities and broader protocol integrations.
This article explains the Model Context Protocol (MCP), its architecture, why it’s needed for seamless AI‑tool interaction, how it differs from traditional function calls, and provides a step‑by‑step guide with Python code to build, test, and debug an MCP server.
Introducing MCP Inspector, a GUI-based debugger for Model Context Protocol (MCP) servers that lets developers visualize tool registrations, prompt templates, resources, and real-time interactions, while providing commands to launch, control, and troubleshoot LLM applications, ultimately streamlining development and reducing debugging friction.
The discussion introduces the Model Context Protocol (MCP) as an open‑source framework that repositions AI from a peripheral add‑on to the central “brain” of software, allowing AI‑first integration with business APIs, reducing rewrites, unifying user interfaces, and promising a rapid open‑source surge that could reshape development, production, and decentralized ecosystems.
The article examines the Model Context Protocol (MCP), highlighting its unnecessary complexity, security vulnerabilities across creation, runtime, and update phases, challenges in tool management, persistent connections, and the need for standardized gateways and server hosting solutions, while referencing recent research and roadmap proposals.
This guide shows how to convert a traditional Spring Boot book‑management service into a Model Context Protocol (MCP) server that lets large language models interact with the service via natural‑language commands, covering dependency setup, MCP configuration, tool annotations, chat client integration, and end‑to‑end testing.
This article explains what Model Context Protocol (MCP) is, demonstrates a simple e‑commerce example, and provides a step‑by‑step guide to create, configure, build, and debug an MCP service using APISIX Admin API, Node.js, TypeScript, and Zod, enabling natural‑language interaction with backend APIs.
The article analyzes the Model Context Protocol (MCP) introduced by Anthropic, detailing its standardized, distributed architecture that overcomes the limitations of Function Calling, and provides step‑by‑step Python examples for building MCP client and server, demonstrating how large language models can reliably interact with external tools.
This article explains the open‑source Model Context Protocol (MCP) created by Anthropic, describes its client‑server architecture for safely linking LLMs with external data sources and tools, and provides a complete step‑by‑step Python tutorial—including environment setup, server and client code—to demonstrate MCP in action.
The Model Context Protocol (MCP) lets large language models safely and directly access diverse data sources and tools, breaking data silos and enabling seamless AI‑driven automation across development, operations, and multi‑agent workflows.
Model Context Protocol (MCP) is a standard client‑server protocol that enables AI models to access real‑time external data and tools, with detailed explanations of its architecture, practical setup steps, code snippets for Figma integration, and a discussion of current limitations and future prospects.
This article introduces the Model Context Protocol (MCP), explains its core features and open‑source implementations, and then compares MCP with Function Call and AI Agent concepts, highlighting their similarities, differences, and practical use‑case examples.
This article explains Anthropic’s open‑source Model Context Protocol (MCP), its client‑server architecture, resource and tool definitions, sampling workflow, and provides step‑by‑step Python examples for building a PoE2 hot‑fix fetcher and a simple chatbot that leverages MCP to connect large language models with external data sources and functions.
The article presents Anthropic’s open‑source Model Context Protocol (MCP) – a client‑server framework that standardizes how large language models securely access resources, prompts, and tools (the “HTTP of AI”) – and demonstrates its use through a hot‑fix scraper and a dynamic chatbot that discovers and invokes tools via JSON‑formatted calls.
This article explains what the Model Context Protocol (MCP) is, lists popular MCP service marketplaces, and provides step‑by‑step instructions for adding global or project‑level MCP servers in Cursor, including JSON configuration, verification, usage examples, tips, and troubleshooting.
This article demonstrates how to implement the Model Context Protocol (MCP) using Spring AI, covering the creation of MCP hosts, clients, and servers, configuring dependencies, integrating Claude, adding Brave Search and filesystem tools, and building a functional chatbot that leverages external data sources through standardized LLM interfaces.
This article explains the data‑isolation problem facing large language models, introduces the Model Context Protocol (MCP) as a standard bridge to external data sources, and provides a step‑by‑step PHP SDK tutorial—including installation, server and client code, and optional advanced logging—to help developers integrate AI models securely and efficiently.
The article explains how the Model Context Protocol (MCP) addresses the difficulty of connecting large language models to external data, tools, and APIs by providing an open, standardized interface that enables AI agents to access real‑time information, act autonomously, and do so securely and modularly.
This guide walks you through installing prerequisites, initializing a Node.js project, implementing the Model Context Protocol server with ListTools and CallTool handlers, configuring Cloudflare Flux API credentials, debugging, and integrating the server into Cursor to enable on‑demand text‑to‑image generation.
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.
The Model Context Protocol (MCP), announced by Anthropic, standardizes how AI applications provide context to LLMs, offering a client‑server architecture that simplifies data and tool integration, and is compared with function calling, highlighting its benefits, workflow, controversies, and future prospects.
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.
