My Experience Learning Model Context Protocol (MCP): A New Way for AI Tools to Connect

What is Model Context Protocol?

When I first heard about Model Context Protocol (MCP), I was confused about what it actually does. After spending some time reading the documentation and trying it out, I can explain it in simple terms.

MCP is a standardized way that allows AI clients and servers to communicate with each other. The main idea is that servers can ask clients for context, tools, and even access to language models. This creates a more flexible system where different AI applications can work together.

I like to think of MCP as similar to how HTTP works for websites - it's a common protocol that lets different AI applications talk to each other, no matter who created them or where they are running.

Why MCP is Important

Before learning about MCP, I noticed that integrating AI tools was always complicated. You usually need to build custom APIs, deal with authentication problems, and handle complex data processing. MCP tries to solve these issues by providing:

1. Standardized Communication: All AI tools can use the same way to interact
2. User-Controlled Access: Users can control which models and permissions to give
3. Context Sharing: Applications can easily share important information with each other
4. Tool Integration: You can access special features across different platforms without difficulty

Applications That Support MCP

I was surprised to discover how many applications already support MCP. The ecosystem is growing quite fast, with support from big AI companies and also independent developers:

MCP Client Applications

• Claude Desktop App: This has complete support for resources, prompt templates, and tool integration
• Continue: An open-source AI code assistant that has MCP support built in
• Cursor: AI code editor that supports MCP tools
• Zed: A high-performance editor with prompt templates and tool integration
• 5ire: Open-source AI assistant that works on multiple platforms

What MCP Can Do

After testing different MCP implementations, I found several useful capabilities:

1. Tool Integration: Servers can make specialized tools available that clients can use
2. Resource Sharing: Clients can give files, images, and other data to servers
3. Sampling: Servers can ask for LLM completions from clients (but users must give permission first)
4. Prompt Templates: Standardized formats that make common AI interactions easier

How to Build with MCP

When I started learning how to add MCP support to applications, I found that the process is not too difficult. Here is a basic example using Java:

// Create a server
McpSyncServer server = McpServer.sync(transportProvider)
    .serverInfo("my-server", "1.0.0")
    .build();

// Define a tool
var calculatorTool = new McpServerFeatures.SyncToolSpecification(
    new Tool("ai-calculator", "Performs calculations using AI", schema),
    (exchange, arguments) -> {
        // Tool implementation
        return new CallToolResult("Result", false);
    }
);

// Add the tool to the server
server.addTool(calculatorTool);

The documentation is quite helpful, although sometimes I had to read it multiple times to understand all the concepts properly.

What I Think Will Happen with MCP

Based on what I have seen in 2025, I think several important things will develop in the MCP ecosystem:

• Standardized Agent Protocols: This might be similar to how TCP/IP changed networking completely
• Cross-vendor Interoperability: Tools will work smoothly across different AI platforms
• Specialized Agent Ecosystems: We will see more domain-specific tools and capabilities
• Enhanced Privacy Controls: Users will get more detailed permissions for AI interactions

Of course, predicting the future is difficult, but these trends seem quite likely based on current development.

My Conclusion

After spending time learning about Model Context Protocol, I believe it represents an important change in how AI applications work together. By giving a standardized way for tools to communicate, MCP is making possible a new generation of AI capabilities that are more powerful, flexible, and user-controlled.

If you are a developer who wants to integrate AI capabilities into your application, or if you are a user wanting to use specialized tools across different platforms, MCP is becoming a very important part of the AI world.

As the protocol continues to develop and more people adopt it, I expect we will see more sophisticated AI ecosystems. These will combine the strengths of different models and tools to solve complex problems in ways that were not possible before.