Mcp

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. ...

If you’re building production AI agents in 2026, you’ve almost certainly encountered both MCP (Model Context Protocol) and Agent Skills as architectural options. Both are ways to extend what an AI agent can do — but they operate at fundamentally different levels of abstraction, and choosing between them (or combining them) is one of the most consequential architectural decisions you’ll make early in a project. This guide breaks down how each approach works, when each excels, the compatibility patterns for using both together, and the production deployment tradeoffs that practitioners are discovering in the field. ...

Running Claude Code in a Docker container isn’t just a development curiosity — it’s increasingly the recommended way to work with AI coding agents in a way that’s both powerful and secure. Docker published an official guide this week walking through the full workflow: local model execution with Docker Model Runner, real-world tool connections via MCP servers, and securing agent autonomy inside isolated sandboxes. This guide synthesizes that walkthrough into a practical tutorial for developers who want to get running quickly. ...

JetBrains developers — the IntelliJ IDEA, PyCharm, GoLand, and Rider community that numbers over 10 million globally — have just received a significant upgrade to their AI coding capabilities. GitHub Copilot’s JetBrains plugin has shipped a major update bringing custom agents, sub-agent coordination, plan agent as generally available features, and MCP auto-approve into preview — closing a meaningful gap with the VS Code Copilot experience. The update was confirmed by the official GitHub Changelog on March 11, 2026, making this one of the most reliably sourced stories this week (99/100 confidence from our Analyst). ...

Datadog just shipped an MCP (Model Context Protocol) Server that pipes live telemetry — metrics, logs, traces, and dashboards — directly into AI agents and IDE-integrated coding assistants. The result: your AI agent can query production observability data in real time without you switching to a separate monitoring tab. This is a significant practical capability. Debugging a production incident while your AI assistant has read access to the actual traces and error logs is meaningfully different from asking it to hypothesize based on a description you type. ...

There’s a category of AI agent project that gets the security architecture right from the start — and NanoClaw is becoming a strong example of what that looks like. The open-source personal AI agent framework has landed support for WhatsApp and Telegram in v0.1.4, bringing container-isolated AI agents directly into the messaging apps most people already live in. What NanoClaw Is (And Isn’t) NanoClaw describes itself as a “minimalist alternative to larger AI agent frameworks.” The numbers back that up: the entire project is roughly 3,900 lines of code across 15 source files, running as a single Node.js process. Compare that to the sprawling architectures of OpenClaw or similar frameworks. ...

The Model Context Protocol just got its most enterprise-important SDK milestone to date. Microsoft’s official C# SDK for MCP has shipped version 1.0, bringing full compliance with the 2025-11-25 MCP specification and — crucially — improved authorization server discovery. For the .NET-heavy enterprise world, this isn’t a footnote. This is the unlocking event that makes serious production MCP deployments viable in environments that have standardized on Microsoft’s stack. What’s Actually New in 1.0 The headline feature is the improved authorization server discovery, which lands just as enterprises are grappling with the identity and access management questions that come with deploying agents at scale. ...

Not every AI workload belongs in the cloud. Liquid AI’s new LocalCowork platform is making a direct bet on that premise — and backing it with a genuinely efficient model architecture that makes local agentic inference practical on consumer hardware. Released March 5, 2026, LocalCowork is an open-source local agentic workflow platform that runs MCP-based agent tasks entirely on-device using Liquid AI’s LFM2-24B-A2B mixture-of-experts model. The headline number: 2 billion active parameters out of 24 billion total. That ratio is what makes local deployment viable. ...

A piece in The New Stack this week has been circulating in agentic AI builder communities: the argument that developers working in production are replacing bloated MCP servers with Markdown skill files and seeing dramatic reductions in token costs and system complexity. The article references Brad Feld’s CompanyOS (open-sourced February 2026) — a real-world multi-agent system running 12 skill files alongside 8 MCP servers — as a case study in the two-layer architecture that’s emerging in serious deployments. ...

ExpressVPN shipped something genuinely novel on March 5, 2026: the first VPN MCP server from any major VPN provider. It lets AI tools read your VPN status and change connection settings directly from development environments — no app-switching, no GUI. It’s a beta, it’s available now, and it’s worth understanding what it actually enables. What the ExpressVPN MCP Server Does The MCP server exposes two core capabilities to any MCP-compatible AI client: ...