The world of AI tooling is moving fast—and PHP is keeping pace. One of the most exciting developments for PHP developers is the rise of MCP servers, which make it easy to connect large language models (LLMs) like GPT-4 to real-world data and systems. Let’s dive into what MCP is, how it works, and how you can build your own MCP server in PHP.
What is MCP?
MCP stands for Model Context Protocol. It’s a way to bridge the gap between an AI model (like a chatbot or coding assistant) and the actual data or tools it needs to complete a task.
- Model: Refers to the AI model itself (text, image, audio models, etc.). For many, this means large language models like GPT-4.
- Context: The crucial piece. LLMs don’t have access to your WordPress backend or your Excel sheets by default. The “context” is what MCP servers provide—giving the AI model access to the data, tools, or environment it needs to perform tasks you want.
- Protocol: Defines how this communication happens. MCP uses JSON-RPC over various transports:
- Standard input/output (for local servers)
- Server-sent events (for networked servers)
- WebSockets (less common so far)
Why Does Context Matter?
Consider asking an AI assistant: “Check my WordPress posts against this Excel file.” The AI doesn’t magically have access to your local files or your WordPress database. It needs a secure, structured way to access those resources—and MCP provides that.
MCP servers act as the trusted middle layer between the AI model and your environment. They fetch, manipulate, or expose the necessary data or tools, so the model can use them in generating answers.
How MCP Works in Practice
When you type a prompt in your favorite IDE or AI assistant (e.g., PHPStorm, VS Code, Cloud Desktop), the client checks available MCP servers for tools and resources. Those MCP servers handle the “real” work—like querying a database, reading a file, or making an API call. The results are sent back to the AI model, which uses them to generate its final response.
Context Types
MCP supports different types of context:
- Tools: These are callable implementations (like API endpoints, CLI commands, or PHP functions). They do things.
- Resources: Static data (like files or database exports) that can be attached to prompts.
- Prompts and Sampling: Protocol-level features for more advanced use cases (less common right now).
A Practical Example
Imagine you’re working on a local WordPress project. You can set up an MCP server that exposes tools like “add post” or “get posts.” In your IDE or AI assistant, you can write:
“Publish this new blog post to my local WordPress installation.”
The MCP client (your IDE) finds the registered “add post” tool on your local MCP server. It runs the command with your input and returns the result to the AI model, which then informs you that your post was successfully published.
This turns AI prompts into real, functional automation.
Building Your Own MCP Server in PHP
Building an MCP server in PHP is surprisingly straightforward, thanks to packages like php-mcp-server.
1. Install the Package
Use Composer to install it in your project.
bashCopyEditcomposer require vendor/php-mcp-server
(Note: Replace with actual package name.)
2. Define Tools as PHP Classes
Define your “tools” as PHP classes with public methods. You can use attributes to describe each method, specifying:
- Name
- Description
- Return type
- Parameters
The MCP server package will parse these attributes and docblocks so your client (IDE or AI tool) can display friendly descriptions and parameter information.
Example Tool
A simple example might be a tool to get Laravel Herd download counts:
phpCopyEdit#[McpTool(
name: 'getHerdDownloads',
description: 'Fetches Laravel Herd download counts.'
)]
public function getDownloads(string $platform = null): int
{
// Your logic here
}
The client will see getHerdDownloads
, with its description and the optional platform
parameter.
3. Define Resources
Resources are static data you want to expose. You can also define them with attributes, specifying things like URI and MIME type. While clients today often require manual resource addition, future clients may support automatic detection.
4. Run the Server
To launch your server, write a small PHP script that:
- Instantiates the MCP server
- Discovers available tools and resources
- Sets up transport (usually standard input/output for local use)
This can be as simple as:
phpCopyEdit$server = new McpServer();
$server->registerTool(new MyTool());
$server->run();
Security Considerations
Once you approve a tool in your client, it can do anything its code allows with your data. That’s the point—but it also means you must trust the author of the MCP server and its code, just like you trust any PHP package you install.
MCP is powerful, but with power comes responsibility. Review, audit, and only use servers you trust.
The Growing MCP Ecosystem
MCP is still new. There are competing packages, evolving standards, and a bit of fragmentation between different clients and transports. But the ecosystem is improving fast:
- Documentation is getting better.
- More clients are adding support.
- Developers are sharing practical tools and patterns.
Real-World Use Cases
Some of the ways developers are already using MCP:
- Browser automation: Triggering headless browsers for testing or scraping.
- Internal backend tools: Automating business processes (e.g., refunding orders, syncing data).
- Local development: Integrating with tools like Laravel Herd to set up environments, run tests, or deploy with simple prompts.
MCP servers can also dynamically add tools at runtime, so new capabilities show up in your AI assistant automatically.
The Future of MCP
It’s easy to imagine a future where many APIs expose an MCP interface directly. Instead of building and maintaining a custom client integration, you could simply point your IDE or AI assistant at the MCP server and get full, structured access to an API’s capabilities.
This would make development faster, safer, and more consistent—giving AI tools the power to actually do things in your environment, not just suggest code.
Final Thoughts
MCP is an exciting new way for PHP developers to bridge AI models with real-world systems and data. By building MCP servers in PHP, you can unlock powerful new workflows in your IDE or assistant—automating everything from posting to WordPress to managing local dev environments.
If you’re a PHP developer exploring AI integration, now is a great time to dive in. The ecosystem is still young, but it’s growing fast, and PHP is well-positioned to be a first-class player in this new world of AI-enhanced development.