The article examines the emerging A2A (Agent‑to‑Agent) protocol, tracing its evolution from function calling to MCP and finally A2A, and evaluates its core concepts, security model, task lifecycle, transport options, design guidelines, and operational best practices for building interoperable AI agent systems.
The article examines Model Context Protocol (MCP), introduced by Anthropic in November 2024, tracing its rapid adoption, architectural design—including Host/Client/Server roles, transport layers, security and observability practices—and outlines production guidelines, future roadmap, and current limitations.
This article explains the Chrome DevTools Protocol (CDP), its WebSocket‑based JSON‑RPC communication model, domain architecture, and how AI agents and tools like Playwright and Puppeteer rely on it, while providing practical TypeScript examples, common pitfalls, and tips for direct CDP usage.
The article dissects the Model Context Protocol (MCP), showing how it transforms the M×N integration explosion into a linear M+N model, details its four-component architecture, compares it with traditional Function Calling, provides a TypeScript server example, and outlines emerging ecosystem and security considerations.
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.
This article explains how the Agent Client Protocol (ACP) standardizes communication between AI coding agents and editors or messaging platforms, describes OpenClaw's acpx plugin that connects ACP to Discord and Telegram, and provides detailed configuration, command reference, comparison with sub‑agents, practical use cases, and troubleshooting guidance.
This article explains the A2A (Agent‑to‑Agent) protocol, AgentCard format, service discovery, JSON‑RPC communication, task lifecycle, credential protection, and provides a step‑by‑step Go SDK demo for building and invoking multi‑agent systems on the AgentRun cloud‑native platform.
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.
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 introduces Workerman‑JsonRpc, a lightweight PHP RPC framework built on Workerman, detailing its key features, environment requirements, installation steps, synchronous and asynchronous client usage, server implementation, and built‑in monitoring capabilities for creating scalable network applications.
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 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 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 guide explains the lightweight, stateless JSON-RPC 2.0 protocol, detailing its request and response structures, error handling, notifications, batch processing, and real‑world use cases such as AI tool integration, blockchain node interaction, and IoT device communication.
This article introduces the JSON-RPC 2.0 protocol, demonstrates how to integrate the jsonrpc4j library into a Spring Boot 3.4.2 project, and provides step‑by‑step code examples for defining services, exposing them via beans or annotations, configuring various client proxies, handling errors, and implementing streaming socket communication.
This article explains the limitations of the original Stdio and HTTP SSE communication modes for MCP, introduces the Streamable HTTP protocol that resolves those issues, and provides a step‑by‑step Python implementation of both a Streamable HTTP MCP server and a matching client, complete with environment setup, FastAPI code, JSON‑RPC handling, and tool‑calling examples.
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.
The MCP 2025‑03‑26 release introduces mandatory OAuth 2.1 with PKCE, a single‑endpoint Streamable HTTP transport, required JSON‑RPC batch processing, richer tool metadata, structured progress notifications, audio multimodal support, and robust session management, all backed by extensive security hardening and performance gains.
The updated MCP protocol (2025‑03‑26) introduces JSON‑RPC batch processing and enhanced tool annotations, enabling AI agents to send multiple requests in a single call, improve performance, and provide richer metadata for safer, more transparent tool usage.
The MCP 2025‑03‑26 release replaces the implicit OAuth 2.0 flow with mandatory OAuth 2.1 PKCE, introduces a single‑endpoint Streamable HTTP protocol, enforces JSON‑RPC batch processing, adds multimodal audio support, tool metadata annotations and progress messaging, and outlines migration steps for developers and operators.
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 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.
This article demystifies the Model Context Protocol (MCP) by explaining its SSE‑based communication flow, showing how to implement a lightweight MCP server in Python, comparing it with REST, MQ and WebSocket, and highlighting its stateful bidirectional RPC model for AI integration.
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 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.
This article explains the fundamentals of device communication, from simple LAN setups using hubs and switches to internet protocols like HTTP, then guides the design of custom communication protocols, illustrates JSON‑RPC over sockets, and explores CAN bus specifics, offering practical code examples and architectural diagrams.
This article explains how to construct and send Ethereum JSON‑RPC calls such as net_version through a WebSocket connection using the Web3j Java library, including source code analysis, request construction, and a practical example with console output.
This article explains the fundamentals of Remote Procedure Call (RPC), explores how data moves across OSI layers, compares HTTP and RPC, and provides a complete Node.js JSON‑RPC example with code, error handling, and real‑world use cases such as Chrome DevTools Protocol.
This article explains how to build a versatile browser message‑communication library that works across iframes, Web Workers, ServiceWorkers, BroadcastChannel and other MessagePort‑like objects, using a unified interface design, TypeScript typings, event‑driven architecture and JSON‑RPC for reliable remote procedure calls.
This article explores the core principles of asynchronous programming, explains how Prefresh enables fast refresh for Preact, showcases a comprehensive web animation tutorial, introduces the Doodle Ipsum illustration placeholder service, and details Theia's JSON‑RPC communication protocol for Cloud IDE extensions.
This article explains how to design, implement, and deploy a high‑performance Go service that uses an upgraded sego tokenizer to load custom sensitive‑word dictionaries, provide JSON‑RPC detection, support hot‑reloading, and scale across multiple data centers for live‑stream platforms.
When sending Ethereum transactions via JSON‑RPC, a mismatched nonce can cause the transaction hash to appear while the transaction remains unconfirmed, staying in the queue indefinitely until the nonce order is corrected.
This guide walks you through using Zabbix's JSON‑RPC API with curl to authenticate, retrieve host and item IDs, fetch historical metrics for specific time ranges, and access additional system information such as version, macros, and media settings.
This article explains how Riot's Game Data Server (GDS) replaces chaotic .ini editing with a JSON‑based RPC system, introduces property macros for type safety, and uses layered versioning to eliminate merge conflicts and simplify multi‑tool workflows for League of Legends development.
