Mcp

What Is Agentjacking? In June 2026, researchers at Tenet Security disclosed a new attack class they called agentjacking — and it’s the most practical AI agent supply chain attack I’ve seen in production. The premise is deceptively simple: an attacker injects a malicious error event into your Sentry project, and when your AI coding agent (Claude Code, Cursor, or OpenAI Codex CLI) reads that event via the Sentry MCP server, it executes the attacker’s embedded payload with your system privileges. ...

If you’re running more than a handful of MCP-backed agents in production, you’ve already hit the wall: point-to-point connections between every agent and every MCP server create an N×M integration nightmare, credential sprawl, and zero visibility into who’s calling what. AWS’s answer in 2026 is a two-layer governance stack — Bedrock AgentCore for per-agent MCP server integration and access control, and the A2A Gateway for centralized agent registry, discovery, routing, and rate limiting across your entire fleet. Here’s how to set it up, what each piece actually does, and where the sharp edges are. ...

If your coding agents can’t see your artifact repository, they’re flying blind. They’ll guess dependency versions, hallucinate package names, and suggest upgrades that don’t exist. But giving an AI agent direct access to Artifactory is a bad idea — one prompt injection and your entire binary repository is an attack surface. JFrog solves this with two complementary paths: JFrog Skills (open-source agent skills) and the JFrog MCP Server (remote SaaS MCP server). Both give agents safe, governed access to artifact context, but they work differently and suit different use cases. Here is how both work, when to use each, and how to set them up without compromising security. ...

If your team is running AI coding agents in production — Claude Code, Cursor, Windsurf, GitHub Copilot — you’ve probably already felt the gap between traditional AppSec and what these agents actually do. Traditional security tools scan committed code. Agents don’t just write code; they install MCP servers, download skills, run shell commands, and make API calls. By the time a traditional SAST scan runs, the damage is already done. ...

SonarQube MCP Server is SonarSource’s official open-source bridge between AI coding agents and SonarQube’s code quality engine. Instead of context-switching to a browser dashboard or running CLI scans manually, your AI agent can query issues, check quality gates, analyze code snippets, review security hotspots, and even change issue status — all through MCP tool calls. Version 1.19.0 ships with 20+ tools, supports every major AI client from GitHub Copilot to Claude Code, and works with both SonarQube Cloud and self-hosted Server instances. ...

AI coding agents ship code faster than ever, but when they break, the debugging experience is nothing like a traditional stack trace. You don’t get a line number and a segfault — you get a silent hang, a garbled terminal, a 529 from the API, or an MCP server that just won’t connect. After spending the last year running Claude Code, Codex CLI, GitHub MCP Server, and Playwright MCP in production pipelines, I’ve collected the debug patterns that actually work. Here’s the field guide I wish I’d had. ...

Dataverse is becoming Microsoft’s governed data and tool layer for enterprise agents: records, metadata, security roles, semantic models, Business Skills, and MCP endpoints in one place. For developers, the practical question is no longer “can my agent reach Dataverse?” It is “which Dataverse interface should this workflow use, and how do I keep it governed?” What does Microsoft mean by Dataverse as an agent data platform? Microsoft’s 2026 positioning is more specific than the older “Dataverse is a business data store” message. The new claim is that Dataverse can give agents business understanding, not just API access. That means an agent should be able to discover tables, understand relationships, respect role-based security, call reusable business processes, and operate through approved tools. ...

The Cursor 3.9 Customize page, shipped June 22, 2026, replaces scattered JSON config files and settings panels with one unified UI for managing plugins, MCP servers, skills, subagents, rules, commands, and hooks. Instead of editing mcp.json by hand or hunting through tabs for rule files, you now open a single sidebar panel and manage every extension category from one place. This guide walks through every feature — scope levels, the plugin marketplace, team leaderboards, MCP server management without JSON, and the new plugin format — so you can configure Cursor for yourself or your entire team in minutes. ...

FastMCP 3.0 is the quickest practical path to a Python MCP server: install the package, wrap typed Python functions with @mcp.tool(), run the server over stdio or HTTP, and connect it to an MCP host such as Claude Desktop or Cursor. Use it when you want agent-accessible tools without hand-writing the low-level protocol. FastMCP 3.0 in 2026: What Is It and Why Do Python Developers Use It? FastMCP 3.0 is a Python framework for building Model Context Protocol servers from ordinary Python functions, resources, and prompt templates. The 3.0 stable release followed two betas, two release candidates, 21 new contributors, and more than 100,000 pre-release installs, which matters because MCP servers are no longer just weekend demos. A FastMCP server lets an AI host call your code through a standard interface instead of brittle shell snippets or custom plugins. In practice, a developer can expose a search function, a database lookup, or a deploy helper as a typed MCP tool and let the host handle discovery and invocation. FastMCP is popular because it hides most protocol ceremony while preserving enough control for production systems: transport choice, composition, authentication, validation, observability, and deployment shape. The takeaway: FastMCP turns Python application logic into agent-ready capabilities with much less glue code than the raw protocol demands. ...

MCP A2A agent coordination in 2026 means using MCP for controlled tool and context access while using A2A for agent discovery, delegation, progress updates, and artifact exchange. The practical move for H2 2026 is not choosing one protocol; it is designing a governed stack where both have clear boundaries. Why Are MCP and A2A Converging in H2 2026? MCP and A2A are converging because production agent systems need two separate layers: a reliable way for agents to use tools, and a reliable way for agents to coordinate with other agents. In April 2026, the Linux Foundation said A2A had passed 150 supporting organizations and had integrations across Google, Microsoft, and AWS platforms, while Anthropic reported more than 10,000 active public MCP servers when donating MCP to the Agentic AI Foundation. Those numbers matter because they show that both protocols are moving from demos into shared infrastructure. MCP standardizes access to resources, prompts, tools, sampling, roots, elicitation, progress, cancellation, logging, and errors. A2A standardizes discovery, tasks, messages, artifacts, and cross-agent handoff. The takeaway is simple: H2 2026 agent platforms will increasingly treat MCP and A2A as complementary protocol layers, not rival standards. ...