💻 Beyond the Hype: A Deep Dive into Model Context Protocol (MCP) and AI Connectivity — Bridging AI Models and Enterprise Systems

Model Context Protocol (MCP) is on its way to becoming for AI agents what REST was for web services — a universal, standardized way to connect and interact. It’s impressive how quickly integration is taking off in the community, with over 2,000 applications already supporting MCP and a rapidly growing adoption rate.

Just as SOAP and later REST simplified web interactions between clients and servers — paving the way for service-oriented architectures and fundamentally transforming how we build and design applications — MCP has the potential to drive a similar shift for AI-enabled interactions.

It standardizes how AI models receive context and interact with external systems and tools, eliminating the need for custom-built bridges.

The internet is flooded with videos and articles proclaiming MCP as a game-changer, but most of them are either marketing hype or tutorials on connecting GitHub with VS Code, or Cursor, claiming that this will “10x your productivity.”

In this article, I want to go beyond the buzz and provide a concrete overview of what MCP really is.

The goal of this article is to save you a ton of time by cutting through marketing slides and superficial tutorials, bringing all core components of MCP together in one place — clear, practical, and free from LLM-generated fluff 🙌

I will focus on one key component of its architecture — MCP Servers — which, in my opinion, should be the main focus for developers and decision-makers right now.

At the end, I’ll also demonstrate the easiest way to get started, showing you how to build your own MCP server with Cloudflare in just five minutes.

Let’s get started!

1. Why MCP, and What Exactly Is It?

Everyone is talking about AI Agents and how they could transform both our professional and personal lives — bringing smart, autonomous helpers at almost no cost.

Depending on the definition, an AI agent differs from a standard ChatGPT session in three key ways:

1. Access to specific data
2. A predefined context/system prompt
3. Specific tools or actions it can perform

In a proof of concept, this is easy to set up — these components are already available with “custom GPTs”.

But scaling agentic solutions brings a wide range of challenges, and every developer ends up solving them in their own way.

For example, if you want to build an agent that can access GitHub, you would need to:

1. Develop a custom data connector to GitHub while handling all security considerations.
2. Define prompts — often more than one — for different scenarios involving GitHub interactions.
3. Specify actions and functions that the agent should be able to perform.

If you’ve successfully handled all three steps, congratulations! You’ve spent a significant amount of time and created your agent. But now, if you need to add a second agent, you’ll quickly realize how complexity of your applications grows exponentially with every new agent.

There are already plenty of frameworks, each following its own paradigm to solve these challenges. However, this has only made interoperability worse, leading to thousands of plugins that don’t work together.

The biggest problem?

YOU are responsible for maintaining all these connectors to third-party tools. That means you need to understand their APIs, security models, and best practices for integration — essentially reinventing the wheel every time..

How Model Context Protocol (MCP) Changes This?

Anthropic’s Model Context Protocol (MCP) shifts this responsibility to solution providers (Image 1). Instead of developers building every integration from scratch, solution providers can now offer standardized connectors, implementing best practices on how to provide application context to LLMs.

With MCP, you can either use existing integrations or easily create your own, all within the same standardized structure.

“MCP standardizes how applications provide context to LLMs.”

MCP enables developers to build AI agents and complex workflows on top of LLMs by providing:

• A growing list of pre-built integrations that LLMs can directly connect to (already over 2,200! Find them in the Smithery — Model Context Protocol Registry)
• Flexibility to switch between LLM providers at any time, without rewriting your code
• Best practices for securing your data within your infrastructure — so you don’t have to reinvent security models

By providing a structured approach, Model Context Protocol reduces complexity and enables seamless integration between AI agents and external tools. In the next section, we’ll dive deeper into MCP Servers — the key component that allows any API-capable solution to connect to the MCP ecosystem.

2. Architecture of MCP: Host, Client, and Server

MCP operates through three core components, each playing a distinct role in enabling AI-driven interactions:

• 🧑‍💻 Host — Any application integrating an LLM, acting as the interface for AI-driven workflows.
• 🤝 Client — Maintains a 1:1 connection between the host and the MCP server, ensuring smooth communication.
• 💻 Server — Bridges the connection between applications and external data sources, transforming raw data into structured, consumable context for AI models.

The first MCP hosts emerged with Claude Desktop, followed by IDEs like VS Code and CursorAI, enabling seamless integration of over 2,000 existing MCP connections directly into development environments.

While there is significant potential in building new hosts that can aggregate and process insights from multiple clients, the bigger opportunity for now lies in creating new MCP servers.

Why Developers and Decision Makers Should Focus on MCP Servers

For developers and product owners responsible for digital solutions, MCP servers represent the most impactful area of innovation. By building servers, you:

✅ Extend MCP’s capabilities by connecting AI to custom APIs, databases, and business processes.
✅ Standardize AI interactions, reducing the complexity of AI-driven automation.
✅ Unlock new AI-powered applications, from industrial automation to smart data processing.

Given MCP’s rapid adoption, now is the perfect time to explore how your products can integrate into this ecosystem. So, let’s dive into MCP servers — the core of scalable AI connectivity!

3. Core Components of an MCP Server

The MCP server is the central component of the architecture.

Structurally, it reminds me a bit of GraphQL — under the hood, your backend can be messy and diverse, but externally, the MCP server provides a beautifully organized interface that delivers structured context from your data sources to LLMs.

The Three Core Capabilities of an MCP Server

An MCP server has three main components:

💾 Resources — File-like data that clients can read (e.g., API responses, file contents).

🧰 Tools — Functions that LLMs can call (with user approval) to perform actions.

📑 Prompts — Pre-written templates that guide users through specific tasks.

Let’s go through them one by one.

1) Setting Up the MCP Server

// Setting Up the MCP Server

const server = new McpServer({
  name: "My Super AI App",
  version: "1.0.0"
},{
  capabilities: {
    //Capabilities of this server -> next step
    resources: {},   

    // Optional -> instructions how to use the
    instructions: '' 
  }
});

First, defining a server is simply an initialization step:

2) Adding Resources 💾

Resources help connect data with LLMs. They represent any kind of structured information that an MCP server makes available to clients, including:

• File contents
• Database records
• API responses
• Live system data
• Screenshots and images
• Log files
• And more

Important: Resources function similarly to GET endpoints in a REST API, meaning they provide data but shouldn’t perform computation or have side effects:

// Static resource
server.resource(
  "config",
  "config://app",
  async (uri) => ({
    contents: [{
      uri: uri.href,
      text: "App configuration here"
    }]
  })
);

// Dynamic resource with parameters
server.resource(
  "user-profile",
  new ResourceTemplate("users://{userId}/profile", 
                                  { list: undefined }),
  async (uri, { userId }) => ({
    contents: [{
      uri: uri.href,
      text: `Profile data for user ${userId}`
    }]
  })
);

3) Adding Tools 🧰

Tools allow LLMs to take actions through your server. Unlike resources, tools are expected to:

• Perform computation
• Trigger actions
• Have side effects (e.g., modifying data, executing workflows)

In MCP, tools allow servers to expose executable functions that can be invoked by clients and used by LLMs to perform actions. Their key capabilities include:

• Discovery — Clients can list all available tools via the /tools/list endpoint.
• Invocation — Tools are executed using the /tools/call endpoint, where the server performs the requested operation and returns results.
• Flexibility — Tools can range from simple calculations to complex API interactions, making it easy to extend an LLM’s capabilities dynamically

// Simple tool with parameters
server.tool(
  "calculate-bmi",
  {
    weightKg: z.number(),
    heightM: z.number()
  },
  async ({ weightKg, heightM }) => ({
    content: [{
      type: "text",
      text: String(weightKg / (heightM * heightM))
    }]
  })
);

// Async tool with external API call
server.tool(
  "fetch-weather",
  { city: z.string() },
  async ({ city }) => {
    const response = await fetch(`https://api.weather.com/${city}`);
    const data = await response.text();
    return {
      content: [{ type: "text", text: data }]
    };
  }
);

4) Defining Prompts 📑

Prompts are reusable templates that help LLMs interact with your server efficiently.

They are a powerful abstraction that can:

• Accept dynamic arguments
• Include context from resources
• Chain multiple interactions
• Guide specific workflows
• Surface as UI elements (e.g., slash commands)

//Here, a simple but still dynamic example on prompts:

server.prompt(
  "review-code",
  { code: z.string() },
  ({ code }) => ({
    messages: [{
      role: "user",
      content: {
        type: "text",
        text: `Please review this code:\n\n${code}`
      }
    }]
  })
);

5) Running Your MCP Server

You’re done! Now you can run your server, depending on your environment.

• Run it locally for direct integration.
• Deploy it remotely with Server-Sent Events (SSE).
• Use specialized services, like Cloudflare (covered in the next chapter).

For completeness, let’s run it on a simple server using Express.js. For remote deployments, start a web server with an SSE endpoint and a separate endpoint for client messages:

import express from "express";
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { SSEServerTransport } from "@modelcontextprotocol/sdk/server/sse.js";

const server = new McpServer({
  name: "example-server",
  version: "1.0.0"
});

// ... set up server resources, tools, and prompts ...

const app = express();

app.get("/sse", async (req, res) => {
  const transport = new SSEServerTransport("/messages", res);
  await server.connect(transport);
});

app.post("/messages", async (req, res) => {
  await transport.handlePostMessage(req, res);
});

app.listen(3001);

6) Bonus: Testing with MCP Inspector

To test your server, you can use MCP Inspector, a lightweight UI developed by Anthropic for debugging MCP servers.

🔍 MCP Inspector is an interactive developer tool that allows you to:

• Test your MCP server in real time.
• Debug interactions between your LLM and external resources.

You can find the tool here: modelcontextprotocol/inspector: Visual testing tool for MCP servers

4. Easy Way: Creating Your Own MCP Server with Cloudflare Workers-MCP

One of the fastest & easiest ways to get started with an MCP server is Cloudflare’s workers-mcp package. You can find the repo here.

This project provides:

• A ready-to-use template
• A CLI tool for quick setup
• In-Worker logic to connect any MCP client directly to a Cloudflare Worker

Since it’s deployed on your own Cloudflare account, you can fully customize it while benefiting from secure, managed infrastructure.

Example: Teaching LLMs to Generate Random Numbers

LLMs struggle with generating truly random numbers. Instead of relying on LLM outputs, let’s create a custom Cloudflare Worker that fetches random numbers from our “ secure random number service”:

/** lets definy MyWorkerMCP, which can: 
*  
*   1) Say hello
*   2) Generate a fake random number
*   '@param' and '@return' will be utlized for the LLM context! 
**/

export class MyWorkerMCP extends WorkerEntrypoint<Env> {
  /**
   * Helps LLMs to Generates REALLY a random number.
   *
   * @return {string} A message containing a super random number
   * */
  async getRandomNumber() {
    return `Your REALLY random number is ${Math.random() + 0.001}`
  }

  
}

Step-by-Step Setup

To get started with Cloudflare and Node.js, simply use npx. The following command will set up the project, including the folder structure and all necessary dependencies:

# Step 1: Generate a new Worker
npx create-cloudflare@latest my-new-worker

# Step 2: Install workers-mcp
cd my-new-worker  
npm install workers-mcp

# Step 3: Run the setup command 🪄
npx workers-mcp setup

Once the project is set up, you can modify the logic inside the provided template to fit your specific needs.

After making changes to your Worker’s code, deploying updates is simple:

npm run deploy

This command updates both Claude’s metadata about your function and your live Cloudflare Worker instance.

Let’s go ahead and deploy our random number generator so our MCP client can call getRandomNumber() from our server!

IMAGE

5. Practical Example: Industrial AI Integration with MCP

Let’s look at a real-world industrial use case where MCP can simplify AI-driven automation in a smart factory environment.

Imagine a manufacturing use case, that aims to integrate predictive maintenance with a LLM-powered workflows. The goal is to allow AI agents to:

1. Access real-time company data (e.g., machine status, maintenance logs). → Resource
2. Schedule maintenance tasks automatically when anomalies are detected. → Tool

With MCP, we can expose an API that enables AI models to retrieve machine data and trigger maintenance workflows securely.

How It Works

💾 Resource: The companyDB resource fetches data from the factory’s internal API, allowing AI models to query real-time machine status, production logs, or sensor data.

🧰 Tool: The scheduleMaintenance tool would let AI agents schedule maintenance by sending a request to the internal system, specifying the machine and the desired maintenance date.

The following MCP server allows LLMs to retrieve factory data and trigger maintenance tasks via API calls:

/co

How is this different to RPA or Chat Bots?

• No need for custom integrations — MCP provides a standardized way to connect AI models to data and automate tasks.
• Scalability — As more use cases adopt MCP, these connectors can be reused, reducing engineering overhead.
• Seamless AI-Agent Operations — with this connectors, AI-powered assistants can monitor equipment, analyze sensor data, and trigger actions, improving efficiency and uptime, where business logic can be defined by prompts, instead of writing hundreds of lines of custom code.

This is just one example of how MCP can bridge AI models with industrial systems, making automation and AI-driven decision-making more seamless than ever.

6. Best Practices for MCP in 2025

The MCP ecosystem is evolving rapidly, but as of 2025, some best practices are already emerging to ensure reliability, security, and scalability.

Here’s a structured approach to following MCP best practices effectively, mainly based on the documentation:

1. Transport Selection

Choosing the right transport method for efficiency and security:

Local Communication → Use stdio transport for processes running on the same machine.

✅ Efficient for local communication
✅ Simple to manage

Remote Communication → Use Server-Sent Events (SSE) for scenarios requiring HTTP compatibility.

✅ Works well over standard web protocols
✅ Requires proper authentication & security considerations

2. Logic Separation

Properly structuring logic avoids unnecessary complexity and ensures maintainability:

• Use Resources for stateless operations for
• Use Tools for processing and data manipulation

💡 Mixing these concepts leads to unmanageable complexity. Keep them separate!

3. Message Handling

A structured approach to handling requests improves reliability:

Request Processing

✅ Validate all inputs thoroughly
✅ Use type-safe schemas to enforce consistency
✅ Handle errors gracefully (don’t return raw exceptions)
✅ Implement timeouts to prevent stuck requests

Progress Reporting

✅ Use progress tokens for long-running operations
✅ Report progress incrementally
✅ Include total progress where possible

Error Management

✅ Use clear and standardized error codes
✅ Provide helpful error messages (avoid vague responses)
✅ Ensure proper resource cleanup on errors

4. Security Considerations

Security should be built into every layer of MCP integration:

Transport Security

✅ Always use TLS for remote connections
✅ Validate connection origins to prevent unauthorized access
✅ Implement authentication when necessary

Message Validation

✅ Validate all incoming messages (avoid injection attacks)
✅ Sanitize inputs to prevent unexpected behavior
✅ Check message size limits to avoid performance issues
✅ Ensure proper JSON-RPC format

Resource Protection

✅ Implement access control policies
✅ Validate resource paths to prevent unauthorized data access
✅ Monitor resource usage to detect abuse
✅ Rate-limit requests to prevent DoS attacks

5. Debugging and Monitoring

A well-monitored system ensures long-term reliability and easier debugging:

Logging

✅ Log protocol events (request/response flow)
✅ Track message processing
✅ Monitor performance to detect slow operations
✅ Record errors for debugging

Diagnostics

✅ Implement health checks for the MCP server
✅ Monitor connection states to detect failures
✅ Track resource usage (memory, CPU, API limits)
✅ Profile performance bottlenecks

Testing

✅ Test different transport methods (stdio, SSE, WebSockets, etc.)
✅ Verify error handling (intentional failures, edge cases)
✅ Check edge cases (unexpected inputs, large requests)
✅ Load test MCP servers under high demand

Following these best practices ensures your MCP server is secure, scalable, and maintainable. As MCP adoption grows, standardization and best practices will play a crucial role in making AI agent ecosystems reliable and efficient.

By structuring logic correctly, validating requests, handling security properly, and ensuring strong monitoring, you future-proof your MCP integrations and build a solid foundation for AI-powered applications.

6. Final Thoughts & Closing Remarks

As we move into 2025, Model Context Protocol is rapidly becoming a foundational technology for AI-driven applications. Just as REST transformed the way we interact with web services, MCP is reshaping how AI models connect, interact, and operate within digital ecosystems.

The adoption of MCP servers enables developers and businesses to standardize AI integrations, reduce complexity, and create more scalable and interoperable AI solutions. By shifting integration efforts to solution providers, MCP makes it easier than ever to build AI-powered applications that can seamlessly interact with real-world data and tools.

Looking Ahead

• For developers: Embracing MCP means focusing on building smart, efficient, and reusable AI integrations rather than custom one-off implementations.
• For decision-makers: MCP provides a framework to future-proof AI applications, ensuring flexibility, security, and interoperability in rapidly evolving AI ecosystems.
• For the industry: As more organizations adopt MCP, we can expect stronger standardization, better tooling, and a growing ecosystem of pre-built integrations that will power the next generation of AI-driven automation.

MCP is not just a trend; it’s a paradigm shift in how AI agents operate and interact with the world. Whether you’re building AI-driven industrial solutions, smart assistants, or entirely new categories of AI applications, MCP servers will be at the core of the transformation.

Now is the time to explore, experiment, and build with Model Context Protocol! 👏