All articles about Mcp
Agent Plugins 1.0.0 packages Agent Skills and MCP servers into one portable format. Five companies shipped the spec without Anthropic, who created both technologies it wraps.
MCP v2 beta ships a stateless architecture. Here is what breaks, which SDK packages to pin, and how to sequence your migration before the 2026-07-28 spec.
MCP v2 Alpha changes stateless routing, SDK pins, authorization, and rollback planning before the July 28, 2026 spec date.
Codex v0.119.0 ships a full plugin system to stable. What MCP Apps, WebRTC voice, and the new extension SDK mean for AI-assisted development teams.
Agent-accessible APIs are the new competitive moat. Learn why MCP integrations, schema-first design, and tool discovery mechanisms determine which SaaS products AI agents will use.
TypeScript SDK v1.27.1, Python SDK v1.26, OpenAI Agents SDK v0.12.x MCP integration, and Google ADK v2.0 Task API — where the MCP ecosystem actually stands in March 2026.
February 2026 reshuffled the AI landscape: Claude Opus 4.6 and GPT-5.3-Codex launched on the same day, Gemini 3.1 Pro followed two weeks later. The result: no single 'best' AI model anymore — but clear lanes for coding, reasoning, and multimodal tasks.
The old app model is dying. 11,393 AI agent tools, 97M MCP downloads per month, 35x CLI token efficiency.
How we built Cortex, a cognitive memory system for AI agents on Convex — with sensory, episodic, and semantic stores, memory decay, spreading activation, and zero infrastructure cost.
Everything we learned building 13 cron jobs, 78 MCP tools, and production skills in OpenClaw. Copy-paste examples, real-world patterns, and hard-won lessons from running autonomous AI workflows.
Apple opens Xcode to external AI agents — marking the beginning of a new era in app development with Claude, Codex, and MCP support.
MCP connects agents to tools. ACP connects agents to each other. Together they form the communication stack for next-gen AI systems. Full comparison with code examples.
Week 4/2026 brings major AI updates: OpenAI tests ChatGPT ads for 800M users, Claude Cowork automates desktop tasks, Google connects Gemini to Gmail and Photos – and critical MCP security flaws reveal new risks for AI agents.
Comprehensive analysis of the most important AI developments in week 3 of January 2026: Apple-Google partnership for Siri with 1.2T parameter Gemini model, Claude Code 2.1.9 security updates, OpenAI Codex v0.86.0 with skills, ChatGPT Health, MCP under Linux Foundation, and critical deprecations. With practical use cases and concrete recommendations.
Complete guide to integrating n8n with Claude Code via the Model Context Protocol (MCP). Learn how to automatically create, validate, and deploy n8n workflows with AI – using just a single prompt.
Learn step by step how to create autonomous AI agents with the Claude Code Agent SDK. This beginner's guide includes practical code examples, from simple chatbots to complex agents with tools, memory, and subagents.
January 2026 brings transformative updates: Claude Code 2.1.0 with skill hot-reload and context fork, OpenAI Connectors and Agent Builder, MCP 1.0 roadmap, and Gemini 3 Flash. Complete analysis of what developers need to know now.
Claude Code 2.1.0 brings revolutionary features for AI developers: Skill Hot-Reload, Context Fork, MCP list_changed notifications, and more. Complete guide with practical examples.
Two fundamental challenges define LLM development in 2026: Mode Collapse reduces output diversity through alignment training, while Context Rot degrades model performance as context windows grow. This article analyzes both phenomena and presents practical solutions like Verbalized Sampling and systematic Context Engineering.
Claude Code Plugins revolutionize AI-powered development. Understand the differences between Skills, Plugins, MCP Servers, and Agents – and extend Claude Code with custom functionality.
My personal selection of the best MCP servers for January 2026: From Desktop Commander to Context7 to Brave Search – these tools transform AI-powered development.
A comprehensive practical guide for implementing AI agents in the financial sector. With complete architecture patterns, production-ready code, and honest assessments of what works and what doesn't.
Context engineering is the discipline of curating, structuring, and defending everything that reaches the LLM at inference time. This comprehensive guide covers 2026 best practices for building reliable AI systems.