MCP

If you use Claude Code, Cursor, or Codex on a codebase larger than a few thousand lines, you’ve felt the pain: every new session starts from zero. The agent greps for a function signature, reads the file, greps for callers, reads more files, and burns through tokens just to reconstruct context you already understood last session. AiDex is an MCP server that tries to fix this with a persistent Tree-sitter code index, semantic search, and cross-session memory. I spent a week testing it against four direct competitors. Here is what I found. ...

By mid-2026, every serious AI coding agent can read your files, search your codebase, and navigate your project structure. The problem isn’t access — it’s that agents spend 40-60% of their tool calls just finding the right code before they can do anything useful with it. I’ve watched Claude Code burn through 15-20 grep and read calls just to understand a single function’s callers and callees. That’s where code index MCP servers come in: they pre-build a structured or semantic map of your codebase so the agent can ask “what calls this function?” in one call instead of ten. ...

If your AI coding agent needs to read a web page, take a screenshot, or crawl a site, you have two options: run a local browser stack (Playwright, Puppeteer) or call a hosted browser API. Cloudflare’s Browser Rendering MCP server splits the difference — it gives you a managed browser in the cloud, exposed as MCP tools your agent can call directly. I’ve been testing all three Cloudflare browser paths — the official Browser Rendering MCP server, the @cloudflare/playwright-mcp Worker, and the CDP-based chrome-devtools-mcp setup — across Claude Code, Cursor, and OpenCode. Here’s what works, what doesn’t, and how to pick the right path. ...

Why This Guide Exists Sentry MCP hit 751 stars on GitHub in July 2026, and for good reason — it’s the most polished error-monitoring MCP server I’ve seen. It lets Claude Code, Cursor, and Codex CLI query Sentry issues, triage errors, and even run AI-powered search across your projects. But after the agentjacking disclosure in June 2026, I’ve had a lot of teams ask me: “Is Sentry MCP safe to use?” ...

Your coding agent trusts the tools it reads. That trust is the vulnerability. When an attacker poisons a Sentry error report, a Datadog monitor alert, a PagerDuty incident, or a Jira ticket description with hidden prompt injection payloads, your agent doesn’t know the difference between a legitimate instruction and a hijack attempt. I’ve spent the last few months digging into this attack surface across the four most common integrations teams wire up to Claude Code, Cursor, and Codex. Here’s what I found and exactly how to fix it. ...

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. ...