MCP

Cloudflare Gateway now classifies MCP traffic by protocol headers instead of hostname heuristics, ships a shadow-MCP dashboard, and lets admins block any MCP connection that does not arrive through an approved portal. The 2026-07-28 stateless spec is what made it possible.

Your coding agent can write the code. With Railway's official MCP server it can ship it too: create the project, deploy the service, assign a domain, tweak variables, and read logs, all as tool calls. The complete one-hour build.

MCP just became stateless, which means your own MCP server is now just an HTTP endpoint that deploys like any web service. Build one with an agent, deploy it on Railway, and point opencode or Claude Code at the public URL. The full build, start to finish.

Cloudflare shipped Radar Researcher, a natural-language agent that answers questions about global internet traffic with real interactive charts. The architecture - MCP code mode, chart specs that never let the model touch raw numbers, and a three-model fallback chain - is the interesting part for developers.

Vercel, OpenAI, GitHub, Microsoft, AWS, and Cursor collaborated on Agent Plugins 1.0.0, an open standard that packages Agent Skills and MCP servers into one portable plugin. ChatGPT, Codex, Cursor, GitHub Copilot, Kiro, and VS Code load the format on day one.

MCP just dropped sessions entirely. Every request is now one self-contained POST. Here is what changed in the 2026-07-28 spec and a Bun + Hono pattern for hosting many MCP servers on a single process.

Vercel MCP now serves both the stateless 2026-07-28 protocol and the 2025 protocol from one endpoint, with mcp-handler 2.x handling the negotiation. The first major hosted MCP server has crossed over - here is what it means for server authors and clients.

MCP Apps shipped with the 2026-07-28 final spec - sandboxed interactive UIs for MCP servers. How they compare to standard tool calling and standalone web UIs, and when to use each approach.

MCP gives an agent live access to tools and data. Agent Skills give it packaged procedure. They solve different halves of the same problem, and the MCP working group is now standardizing how skills ship over MCP. Here is the decision rule.

The late-July Codex and Claude Code updates point in the same direction: coding agents are competing on approval modes, resumable work, MCP auth, artifacts, and review surfaces as much as raw model quality.

A practical comparison of the four authentication platforms developers reach for when connecting AI agents to third-party APIs: Arcade, Composio, Nango, and Stytch. OAuth 2.1, MCP support, integration counts, and which to pick by workload.

The DataFlow-Harness paper is a useful reminder that coding agents should not just emit scripts. For data work, the durable artifact is an editable, validated pipeline.

Claude Code, Claude Desktop, Cursor, VS Code, Zed, and opencode all speak MCP differently. Here is how their transport, auth, and tool-limit support compares.

Apple's Safari MCP server lets AI coding agents inspect pages, capture screenshots, evaluate JavaScript, and run accessibility checks directly in Safari. Complete setup guide with installation, available tools, and practical workflows.

Claude Code and Codex both ship great agents and terrible transcripts. AgentCanvas is a visual adapter that puts the artifacts, decisions, and handoffs on one board so the next agent and the next human can see them.

MCP makes tools callable by agents. That solves invocation. It does not solve visibility. The next agent and the next human still need to see what the tool calls produced, and a transcript is the wrong place for that.

Skills gave an agent what to know. The missing half is what role to play. Agent Studio lets you author subagents next to your skills in one place, serve both over the same MCP endpoint with the same progressive disclosure, browse them over REST and the dd CLI, and publish them to the community under a moderation loop. Here is the design and why the two belong in one studio.

Skills, files, memory, and generation do not need four integrations. They need one MCP endpoint with tiered disclosure, one API key that scopes everything to its owner, and one credit balance. The same tools answer to an MCP client, an in-product chat, and a CLI. Here is the whole architecture, and why it is the shape that makes a fleet of agents coherent.

A decision framework for 2026: MCP servers give an agent access to a live system, Agent Skills teach it how to do a task. Here is when to build each, when to build both, and the criteria that actually decide it, grounded in the MCP spec and Anthropic's skills docs.

The first version of skills-over-MCP served a fixed first-party catalog. Skill Studio extends it two ways: anyone can author skills that ride the same progressive-disclosure endpoint scoped to their own API key, and a skill file can be a link instead of a copy - a URL whose bytes are only fetched at the moment an agent decides it needs them. Progressive disclosure stops at the skill boundary no longer. It runs out to the open web.