The arrival of WordPress 6.9 is one of the most exciting updates for me right now, as it introduces a wealth of new features that represent a significant leap forward for both developers and content creators. These enhancements will not only make your daily workflow more convenient but also demonstrably faster and more efficient.
It comes packed with new blocks, functional improvements, performance optimizations, accessibility fixes, and various developer tools. If you’re curious about the specifics, I highly recommend Kinsta’s summary – I don’t think you’ll find a more comprehensive overview.
I had already tested the system in beta weeks ago, so by the time the final version dropped, I knew exactly what to expect and which features to prioritize. One such priority is the MCP Server, which has become an indispensable part of our current web development workflow. Today, I finished writing a dedicated connector plugin for it, which allows me to generate and manage website content directly through AI software.
What is MCP Server and why is it revolutionary?
Before diving into the technicalities, it’s worth reviewing what exactly an MCP Server is and why it matters. Until late 2024, one of the biggest hurdles in the AI world was that Large Language Models (LLMs) operated in a closed environment. Despite their intelligence, they lacked access to real-time data and couldn’t perform actual operations on external systems. This barrier was dismantled by Anthropic in November 2024 with the introduction of the Model Context Protocol (MCP).
MCP is an open-source standard that acts as a unified “connector” between AI and the outside world. Think of it as a universal intermediary layer. Instead of writing a bespoke integration for every single data source, MCP provides a standardized protocol for AI communication. It functions like an elite interpreter: since an AI cannot browse GitHub or open Figma files natively, it sends a standardized request through MCP. The MCP Server interprets this request, executes the task, and returns the live data. This makes AI not just a theoretical assistant, but an agent capable of performing real-world tasks, such as:
- Retrieving or uploading Google Drive files.
- Fetching live Figma designs.
- Requesting MailerLite campaign metrics.
- Accessing a WordPress site to perform administrative actions.
The Three Core Functions of MCP
What can you actually do with an MCP Server?
- Resources (Read-only): It retrieves data without altering it. Examples include database queries, reading local files, or performing web searches.
- Tools (Write & Action): Actions with real impact. Examples include adding Figma comments, publishing WordPress posts, sending emails via MailerLite, or triggering API calls.
- Prompts (Templates): Reusable workflows for complex, multi-step tasks. For instance, a “Weekly Performance Report” template can query five different databases, aggregate the results, and post a summary to Slack.
Real-world Examples in Development
- GitHub MCP: PR reviews, code analysis, and automated issue tagging.
- Figma MCP: Real-time design retrieval and automatic React code generation.
- WordPress MCP: Content publishing, SEO auditing, and managing Yoast settings via AI.
- Slack & WhatsApp MCP: Automated customer service and channel summaries.
Why This Matters for Web Developers
As a developer, it’s crucial to understand that this is a paradigm shift. Previous AI chatbots relied on static, pre-trained knowledge. With MCP, AI has immediate access to live data, allowing it to interact with systems in real time. This enables seamless code analysis, debugging, and the automation of entire development cycles. It feels as though you have an entire team of developers working alongside you, but at a much higher velocity.
This is particularly transformative in fields involving massive amounts of data—legal texts, technical specifications, or research results—that would take humans weeks to process. AI can organize and summarize this information in seconds, which is why its adoption is skyrocketing in finance, medicine, engineering, and education.
Smart AI Automation
The real power of MCP lies in real-time automation. An AI can now set up a WordPress environment, install plugins, and configure a site based on client requirements automatically. It can monitor code for errors, run SEO audits, and filter broken links before a project even goes live.
However, these are advanced features that demand significant resources. Basic AI tiers won’t cut it; even a Pro subscription can hit its daily limit within hours when running complex MCP workflows. Furthermore, some technical background is essential. You don’t necessarily need to be a master coder to write the prompts, but you must understand the underlying processes—much like a software architect—to ensure the automation is cohesive and effective.
Proven Results: Nine Websites in Six Months
In July 2025, we transitioned entirely to an AI-driven workflow. As a result, I built 9 complete websites in the second half of the year—faster and more efficiently than ever before. This isn’t marketing hype; these sites are live, serving real customers, and the entire process has been demonstrated in our workshops. This level of output simply isn’t possible through manual labor alone.
We now utilize dedicated AI agents for every phase:
- Planning & Strategy
- Copywriting & Content
- Development & Coding
- Marketing Implementation
Risks: Don’t Be Reckless, But Don’t Be Afraid
The danger of MCP is that it works with live systems. You aren’t in a sandbox; you are interacting with production databases and live repositories. A wrong command can have real-world consequences.
The “I’m too afraid to start” attitude is just as detrimental as the “I’ll let the AI handle everything” mentality. As developers, our role is shifting: we are no longer paid just to write lines of code, but to take responsibility for the system’s integrity, security, and performance.
MCP is a young technology. There will be bugs. AI will occasionally misunderstand a prompt or interpret a request too creatively. That is normal. The goal isn’t to achieve a 0% failure rate—it’s to ensure that when a failure occurs, you have the expertise to restore the system within five minutes.
If you can do that, you’re ready for the future of development.
