Model-Context-Protocol

The Model Context Protocol (MCP) has published its new authorization specification, and it’s a meaningful upgrade for anyone building or securing AI agent systems. The spec mandates OAuth 2.1 as the foundational auth framework, with RFC 8707 Resource Indicators providing the mechanism for strict, per-resource access controls. If you’re running MCP servers in production — or building agents that communicate with them — this guide walks through what changed, why it matters, and what you need to implement. ...

Google just made Google Colab a first-class citizen in the agentic AI ecosystem. The company released the open-source Colab MCP Server, enabling any MCP-compatible AI agent to directly interact with cloud notebooks — creating cells, executing code, managing dependencies, spinning up GPUs — all programmatically, without a human touching the browser. This is a meaningful infrastructure shift. Colab has long been the go-to sandbox for ML experimentation: free (and paid) GPUs, a pre-configured Python environment, easy sharing. But it’s always been a human tool. You open it. You run cells. You scroll through outputs. The Colab MCP Server breaks that assumption entirely. ...

Setting up AI agents in most platforms still looks a lot like configuring infrastructure: YAML files, JSON configs, deployment scripts, role definitions in nested attribute hierarchies. It’s powerful, but it’s a specialist skill that most team members don’t have — and it creates a bottleneck every time someone needs to add, modify, or remove an agent. OpenClaw.Direct wants to eliminate that bottleneck entirely. The company launched a Model Context Protocol (MCP) server that lets teams hire, train, and fire AI employees through natural conversation in Claude Desktop and ChatGPT. ...

Pinterest has quietly become one of the first major consumer platforms to deploy the Model Context Protocol (MCP) at genuine production scale — not as a proof-of-concept or demo, but as live infrastructure that engineering teams use daily to automate complex internal tasks. The news, reported by InfoQ this week, is a significant data point for anyone betting on MCP as the standard interface layer for enterprise AI agent integration. ...

The wall between AI agents and your browser just came down. Opera announced today that Opera Neon — the company’s experimental AI-first browser — now supports the Model Context Protocol (MCP) as a native server. This means external AI clients — including Claude Code, ChatGPT, n8n, Lovable, and OpenClaw — can connect directly to a live Neon browser session, access your real-time web context, and take actions inside pages. No Playwright. No Selenium. No screenshots copied and pasted between apps. Just agents talking directly to your browser. ...

A landmark empirical study from the UK’s AI Security Institute — co-authored with the Bank of England — has just published the most rigorous large-scale measurement of AI agent behavior to date. The paper, titled “How are AI agents used? Evidence from 177,000 MCP tools,” analyzed 177,436 Model Context Protocol (MCP) tools created between November 2024 and February 2026. The headline finding: AI agents have decisively crossed from observation to action, and the enterprise security community is not keeping pace. ...

Figma just made a significant move: the design canvas is now open to AI coding agents via a native MCP (Model Context Protocol) server. As of this week, agents like Claude Code, Cursor, VS Code Copilot, Codex, and Warp can read your Figma files, understand the design structure, and generate code that maps directly to your actual components — not a screenshot approximation, but the live design graph. This is currently in free beta. Here’s how to get connected. ...

The Model Context Protocol (MCP) was supposed to be the universal connector for agentic AI — a standard way for agents to call tools without custom glue code. But at Ask 2026, Perplexity CTO Denis Yarats dropped a significant signal: Perplexity is moving away from MCP internally, and the reason has major implications for anyone building production agentic systems. The Problem: 55,000 Tokens Before Your Agent Does Anything Yarats was direct about the technical issue. MCP tool definitions — the schema declarations that tell an agent what tools are available and how to call them — were consuming 55,000+ tokens before a single user message was processed. ...

When Chrome ships a feature, it ships to roughly 3.4 billion browsers simultaneously. That’s what makes Chrome 146’s native Model Context Protocol (MCP) support such a significant — and potentially consequential — development for the agentic AI ecosystem. What WebMCP Actually Is MCP, for those who need the refresher: it’s Anthropic’s open protocol for connecting AI models to external tools and data sources in a standardized way. The “Web” prefix in WebMCP specifically means browser sessions — live, authenticated, cookie-bearing browser sessions. ...

The Model Context Protocol has had a remarkable year. What started as Anthropic’s attempt to standardize how AI models connect to external tools and data sources has become, almost by accident, the de-facto tool layer for the entire agentic AI ecosystem. Claude uses it. OpenAI-compatible agents use it. Builders across the industry are shipping MCP servers like it’s the new API endpoint. But if you’ve tried to run MCP seriously in production, you’ve bumped into the same set of friction points. Authentication is awkward. Streaming is limited. Discovering MCP servers requires manual configuration. Multi-agent handoffs lack proper task lifecycle semantics. And when things fail — network blips, agent restarts, timeout conditions — the retry behavior is undefined. ...