The problem MCP solves
AI models are powerful, but they live in isolation. They can generate text, answer questions, and perform analyses, but they have no direct access to your business data, your CRM, your accounting system, or your internal tools. Until now, every integration required a custom connection — time-consuming and fragile.
Model Context Protocol (MCP) changes that. It is an open standard, originally developed by Anthropic, that provides a universal interface between AI models and external systems. Instead of building dozens of separate connections, you build one MCP server that tells the AI model which tools are available and how to use them.
How MCP works
MCP follows a client-server architecture:
- MCP Host — The application where the AI model runs. Think of a chat interface, an AI assistant within your software, or an automation platform.
- MCP Client — The connection layer that communicates with MCP servers. Each client has a one-to-one relationship with a server.
- MCP Server — The server that exposes specific functionality. This is where you connect your business logic. An MCP server can offer tools, resources, and prompts.
The protocol defines three core primitives:
Tools
Actions the AI model can perform. Think of "look up a customer," "create an invoice," or "fetch inventory status." The model discovers which tools are available and determines on its own when it makes sense to use them.
Resources
Data the AI model can consult. Think of documents, database records, or configuration files. Resources give the model context without requiring it to actively perform an action.
Prompts
Pre-defined templates that control how the model should respond in specific scenarios. This gives you control over the output without completely restricting the model.
MCP is to AI what USB was to hardware: a universal standard that drastically simplifies connecting systems.
Why MCP matters for your business
Your existing software becomes AI-ready
With an MCP server on top of your existing application, you make your software accessible to AI models. You do not need to rebuild your current architecture. The MCP server acts as a layer that exposes your existing functionality through the protocol.
Faster integrations
Where a traditional AI integration could take weeks of development, an MCP server for an existing system can often be set up in days. The protocol is standardized, the tooling is mature, and the documentation is extensive.
Vendor-independent
MCP is an open standard. Your MCP server works with Claude, but also with other models that support the protocol. You are not locked into a single AI provider.
Control and security
You determine what the AI model can and may do. Every tool has permissions, every action can be logged, and sensitive data can be restricted. The model can only do what you explicitly allow.
Practical examples
What does this look like in practice? A few scenarios:
- Customer service — An AI assistant that has access to your customer database, order history, and FAQ via MCP. The assistant can look up customer details, check order status, and provide relevant answers — without you needing to program every scenario upfront.
- Internal tooling — Employees asking questions to your business software in natural language. "How many orders came in this week?" or "Which invoices are still open for customer X?" — the AI model runs the right queries via MCP tools.
- Process automation — AI that analyzes documents and automatically performs the right actions in your system based on their content: create an invoice, place an order, or send a notification.
- Data analysis — An AI model that accesses your reporting data via MCP and performs analyses on demand, identifies trends, or generates summaries.
Building MCP on your existing software
Building an MCP server on top of an existing Laravel application is relatively straightforward. At Coding Agency Meppel, we have implemented MCP servers for multiple clients. The server defines which tools are available, what parameters they expect, and what they return. The implementation behind each tool is your existing business logic — you reuse what you already have.
The steps in broad strokes:
- Determine which functionality you want to expose via MCP
- Define the tools with clear names, descriptions, and parameters
- Implement the tooling on top of your existing services and repositories
- Configure authentication and permissions
- Test the integration with an MCP client
The result is that your existing software — without a major rebuild — becomes accessible to AI models.
Security
Security is a justified concern with any AI integration. MCP addresses this at multiple levels:
- Authentication — MCP servers can use OAuth, API keys, or other authentication mechanisms
- Permissions — Per tool, you control who has access and which actions are allowed
- Input validation — All input from the AI model is validated before it reaches your system
- Logging — Every interaction can be logged for audit and debugging
- Rate limiting — Limit the number of requests to prevent abuse
MCP puts you in control: the AI model can only do what you explicitly allow. Nothing more, nothing less.
Conclusion
MCP is not hype but a fundamental shift in how AI models collaborate with business software. The standard is mature, adoption is growing rapidly, and implementation is manageable. If you want to deploy AI in your organization, MCP is the way to make your existing systems AI-ready without rebuilding everything.
Want to know how MCP can make your business software smarter? Contact Coding Agency Meppel — we would be happy to build it for you.
