Understanding the Basics of MCP
The Model Context Protocol (MCP) is becoming the underlying framework for how AI tools connect with everyday software, quietly transforming the way intelligence integrates into digital infrastructure.
Introduced by Anthropic in 2024, the MCP standardizes the conversation between AI models and the systems they interact with, including tools, APIs, databases, and other components. It works like a universal connector, doing for AI integration what USB-C did for device compatibility. Instead of building custom links between every model and every service, MCP establishes a shared language that both sides can rely on.
Before this, the landscape was fragmented. AI systems relied on hand-coded plugins, vendor-specific bridges, or one-off integrations that barely scaled. Managing a few connections was possible. Managing dozens or hundreds was messy. MCP brings order to that mess, turning scattered touchpoints into a structured, repeatable protocol.
How MCP Works Behind the Scenes
Under the hood, MCP follows a client-server structure. The AI model typically acts as the client—it initiates requests. The server, on the other hand, is a lightweight interface that knows how to expose a system’s capabilities in a way the AI can understand.
The process begins with discovery. The AI doesn’t guess how to use a service. It asks. The server provides a list of available functions—called “tools”—with all the necessary parameters. Each tool is like a pre-defined capability: fetch data, create a file, post a blog entry, query a customer record.
Once discovery is complete, the AI can invoke any of those tools in structured requests. The server processes the task and sends a response, also in a clean, structured format. No screen scraping. No guesswork. No hallucination. Just defined inputs, outputs, and expected outcomes.
What makes this particularly powerful is how it aligns with existing architectural patterns, such as JSON-RPC, which developers already use to build scalable and modular systems. MCP slides into that ecosystem rather than trying to reinvent it.
Why MCP Is a Turning Point for AI Integration
There’s a quiet but fundamental shift happening here. AI isn’t just answering questions anymore—it’s participating. It’s executing tasks, triggering workflows, and interacting with systems like a trained employee rather than a glorified autocomplete.
MCP accelerates that shift by making integrations fast, secure, and repeatable. Instead of writing a new adapter every time an AI model needs access to a different service, developers can implement the MCP spec once and instantly support any compliant model.
Even more crucially, MCP enables models to reason with up-to-date context. Instead of relying on static training data or scraping web pages, a model can request live inventory from a warehouse system, trigger a refund in a CRM, or check the status of a project in real time with reliable results.
This isn’t about smarter predictions. It’s about smarter actions, grounded in the actual systems that run a business.
Real-World Applications Across Industries
In AI Development and Deployment
MCP has already seen rapid adoption by key players. Anthropic’s Claude uses MCP to interact with web services in a structured way, creating posts, pulling records, and generating content with live input from systems. Microsoft’s integrations in Azure OpenAI utilize MCP to enable language models to communicate directly with enterprise applications, such as databases, project management platforms, and analytics dashboards.
What used to require bespoke connectors and prompt engineering can now be handled by structured tools exposed via MCP. It’s a shift from prompting to performing.
In Software Architecture
Architects building complex enterprise systems are finding in MCP a way to make AI a true system participant. Each microservice or external system can expose a set of capabilities. The model, whether it’s used for coding, support, or analytics, simply connects to those tools via MCP and gets to work.
This reduces coupling between systems, improves maintainability, and fits naturally into modern infrastructure stacks. Want to change the AI provider? No problem. As long as both sides speak MCP, everything still works.
Security is baked into the design, too. Tools can define which functions are exposed, what inputs are allowed, and under what conditions actions are authorized. It’s not a free-for-all—it’s structured, secure, and controllable.
In Content Management Systems Like WordPress
WordPress has taken a proactive stance on MCP. With the launch of an MCP Adapter plugin, WordPress can now act as an MCP server, meaning AI tools can interact with a site through officially exposed functions.
An AI assistant could create posts, edit drafts, moderate comments, or update metadata using standardized MCP requests. It doesn’t guess how to use WordPress—it discovers the capabilities, sees what’s available, and follows the rules.
This isn’t just theoretical. Early implementations demonstrate an AI assistant publishing content, summarizing existing pages, and assisting in managing the editorial workflow. Each task runs through the same WordPress REST API that developers already use, and MCP simply wraps that in a discoverable interface.
The WordPress Opportunity with MCP
A New Layer for Extensibility
WordPress thrives on extensibility. Plugins add functionality, APIs expose content, and themes shape the front-end. MCP introduces a fourth layer: AI operability.
With MCP, any plugin can expose its functionality to AI agents using the Abilities API. That SEO plugin offering keyword suggestions? Now it can register those as callable tools. A newsletter plugin that sends campaigns? Also available to a model with the right context.
Even without explicit support, WordPress’s REST API can be scanned and converted into MCP tools. So existing systems are already halfway there. Developers don’t need to reinvent anything—just declare what already exists in a format the AI can use.
Smarter Workflows in Content Management
MCP isn’t just about automation. It’s about collaborative workflows. Editorial teams can work alongside AI agents that understand publishing rules, site structure, and taxonomy, allowing for more efficient and effective content creation.
An assistant might suggest post titles, fetch analytics data, create custom excerpts, or even coordinate with other tools—like image generators or grammar checkers—through a shared MCP interface.
And because each step is standardized, it’s safe to automate or to pause for human input. The AI doesn’t take over. It augments, accelerates, and aligns with existing content workflows.
Built on More Than Protocols: AI Building Blocks in WordPress
The push to integrate MCP in WordPress is part of a larger shift toward AI-native infrastructure. That broader vision is being realized through a set of foundational projects known as AI Building Blocks, a collection of open-source tools designed to bring intelligent capabilities to the WordPress ecosystem.
These building blocks go beyond proof-of-concept. They introduce standardized components for AI integration that work in tandem with MCP to support everything from content creation to agent-based workflows.
Building on What MCP Enables
Protocols often don’t get the spotlight. But they shape everything. HTTP didn’t become essential overnight—it grew as systems adopted it, and suddenly everything was connected. MCP is quietly doing the same for AI.
It removes the friction between models and tools. It creates an ecosystem of interoperable components. And it allows developers to focus on building useful systems rather than figuring out how to wire everything together.
That’s why tech companies from OpenAI to Microsoft to WordPress are embracing it. Not because it’s flashy but because it works. It standardizes intelligence as a feature, not a black box.
As more systems adopt MCP, expect to see AI agents managing dashboards, coordinating between platforms, and handling the kind of work that usually lives in the gaps between software tools.
Whether the goal is to simplify editorial processes in WordPress, unlock new developer workflows, or bring structure to AI system design, MCP brings order to the chaos.
Trew Knowledge specializes in building intelligent, extensible, and AI-integrated digital experiences. Get in touch to explore how AI can enhance your systems—from WordPress to enterprise-scale solutions.
