MCP

The 2026-07-28 Model Context Protocol spec is the largest revision since launch: a stateless core, deprecated Roots/Sampling/Logging, MCP Apps, Tasks, and tougher OAuth. Here is what breaks, what to adopt, and a migration checklist for server authors and client integrators before the July 28 deadline.

developersdigest.tech now speaks MCP. Any MCP-capable harness can call the site's tools directly - generate media, pull vetted skills and agents on demand, persist memory across sessions, search the content, and count tokens. Here is what shipped and how to connect.

SKILL.md solved knowledge packaging with progressive disclosure. MCP solved capability transport but ships flat, context-hungry tool lists. The next shape combines them - an MCP server whose tools are a skill directory, so an agent pays context only for what the task needs. Here is the argument and a working implementation.

A hosted infinite canvas your headless AI agents drive over MCP. Any MCP-speaking agent - Claude Code, Codex, Cursor, or a script - creates HTML docs, images, and video on a live canvas, streamed in as it builds.

Bumblebee is Perplexity's open source scanner for detecting compromised packages, extensions, and MCP configs on developer machines. A read-only Go binary that checks npm, PyPI, Go modules, and 10+ ecosystems against exposure catalogs - without running any install scripts. Here is how to set it up and use it.

Arcade just raised $60M to become the secure action layer for production AI agents. Here is what their MCP runtime actually does, how it differs from rolling your own OAuth, and when to use it.

The Linux Foundation's Agent Name Service proposal points at a real gap in AI agent infrastructure: agents need verifiable identity, scoped capabilities, revocation, and audit trails before they can safely act across tools.

GitHub's Agent Finder discovers and invokes Claude, Codex, MCP servers, and skills automatically. Here is how the new ARD specification changes AI coding tool integration.

The MCP 2026-07-28 final spec is here - sessions are gone, the protocol is stateless. Here is what changed, what broke, and how to finish migrating your MCP servers.

MCP's new enterprise-managed authorization flow is not just less login friction. It moves agent tool access into identity, policy, and audit systems enterprises already understand.

MCP's new Enterprise-Managed Authorization removes per-user OAuth friction. Anthropic, Okta, Figma, and Linear ship centralized auth for AI agent tooling.

JetBrains released Mellum2 on June 2, 2026 - a 12B MoE model with only 2.5B active parameters per token. Here is how to run it locally, when to use it, and where it fits in your AI coding stack.

On June 2, 2026, GitHub made the Copilot SDK generally available. It exposes the same agent runtime behind Copilot - planning, tool calls, file edits, streaming, MCP - across TypeScript, Python, Go, .NET, Rust, and Java. Here is what changed at GA and what it means for builders.

On June 16, 2026, Microsoft's Work IQ APIs reach general availability - a workplace intelligence layer that hands agents pre-assembled, permission-trimmed Microsoft 365 context instead of raw Graph calls. Here is what the four domains, three protocols, and consumption pricing mean for developers building enterprise agents.

OpenCode is the fastest-growing open-source AI coding agent - 160K GitHub stars, 7.5M monthly users, 75+ model providers. Here is how to set it up, configure models, and use it effectively in your workflow.

Chrome 149 ships an origin trial for WebMCP - a proposed web standard that lets developers expose JavaScript functions and HTML forms to AI agents. Here is what it does, how to implement it, and why it matters for the future of agentic browsing.

Open-source AI agent built in Rust, now governed by the Agentic AI Foundation at the Linux Foundation. Desktop app, CLI, and API. 15+ model providers, 70+ MCP extensions.

Headroom is a context compression layer that intercepts your AI agent's tool outputs and strips 60-95% of the tokens before they hit the model - with benchmarked accuracy preserved.

Before an AI agent gets tools, files, APIs, MCP servers, or deployment access, decide what it can read, write, call, log, and roll back.

Prompt injection stops being an abstract LLM risk once an agent can call tools. The practical defense is data boundaries, structured handoffs, tool guardrails, and approval gates around side effects.