What is the Model Context Protocol?
Developed to solve the "N x M" integration problem, MCP allows any AI client (like Claude, ChatGPT, or Cursor) to securely interact with any MCP Server. In a Laravel context, your application becomes the server, exposing its internal "Resources" (Models) and "Tools" (Controller logic) to the AI agents.
The Multi-Agent Architecture
In a multi-agent system, we don't have one giant "do-it-all" AI. Instead, we use:
- The Router Agent: Decides which specialist to call.
- The Worker Agents: Specialized MCP servers for specific tasks (e.g., an "Invoice Agent" or a "Log Analyzer Agent").
- The Evaluator Agent: Checks the work before it reaches the user.
Step-by-Step: Implementing a Multi-Agent System
We will build an MCP server in Laravel that exposes a "Database Tool" and a "Notification Tool" for our agents to use.
Step 1: Install Laravel MCP SDK
First, install the official Laravel SDK for building MCP servers:
composer require laravel/mcpphp artisan vendor:publish --tag=ai-routesStep 2: Define an MCP Tool
An MCP Tool is essentially a function that an AI agent can call. Let's create a tool that allows an agent to fetch user subscription status. Create app/AI/Tools/SubscriptionTool.php.
<?php
namespace App\AI\Tools;
use App\Models\User;
use Laravel\Mcp\Attributes\Tool;
class SubscriptionTool
{
#[Tool(description: "Fetch the subscription plan for a specific user by email")]
public function getStatus(string $email): array
{
$user = User::where('email', $email)->first();
return [
'name' => $user->name,
'plan' => $user->subscription_plan,
'active' => $user->is_active,
];
}
}
Step 3: Register the Multi-Agent Dispatcher
We need a central "Manager" that uses the Laravel AI SDK to orchestrate these agents. Create a service file at app/Services/AgentOrchestrator.php.
<?php
namespace App\Services;
use Laravel\Ai\Facades\Ai;
use App\AI\Tools\SubscriptionTool;
class AgentOrchestrator
{
public function handleTask(string $userRequest)
{
// The Orchestrator agent decides how to use the MCP tools
return Ai::agent("Orchestrator")
->instructions(
"You are a system coordinator. Use the provided tools to fulfill requests."
)
->tools([new SubscriptionTool()])
->prompt($userRequest);
}
}
Step 4: Create the Frontend Interface
We need a simple interface to talk to our agent. Create a Blade file at resources/views/ai/chat.blade.php.
<div id="chat-window">
<input type="text" id="user-input" placeholder="Ask the Agent..." />
<button onclick="sendMessage()">Send</button>
</div>
<script>
async function sendMessage() {
const input = document.getElementById('user-input').value;
const response = await fetch('/ai/dispatch', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ prompt: input })
});
const data = await response.json();
alert("Agent Response: " + data.message);
}
</script>
Step 5: The SQL Component (Resource Discovery)
MCP also allows agents to "browse" your data as Resources. Add this to your mcp.php config to allow agents to see your DB schema.
-- MCP uses this to understand your data context
SELECT table_name, column_name, data_type
FROM information_schema.columns
WHERE table_schema = 'your_database_name';
Why This is the Future of Web Development
By building your application with Model Context Protocol in mind, you are future-proofing your business. In 2026, users won't click through 10 pages to find an invoice; they will simply tell their browser agent, "Find my last invoice and summarize it," and your Laravel app will provide the answer via its MCP server.
Conclusion
Building a Multi-Agent AI System doesn't require a total rewrite. With Laravel MCP, you simply expose your existing logic as tools and let the LLM handle the orchestration. This modular approach ensures your Software Engineering remains clean while providing cutting-edge AI capabilities to your users.
Ready to turn your app into an AI-native powerhouse? Start by creating your first Laravel MCP tool today!