Building Bridges for AI: How to Create MCP Servers for Any Programming Language
Z
Zack Saadioui
8/12/2025
Building Bridges for AI: How to Create MCP Servers for Any Programming Language
Hey there! So, you've been hearing a lot about AI agents, large language models, & all the incredible things they can do. But if you've ever tried to get an AI to interact with a real-world tool or your own custom data source, you've probably hit a wall. It's like trying to plug a European charger into a US outlet – they just don't speak the same language. This is a HUGE problem in the AI world, & honestly, it's been holding back a lot of cool innovations.
But what if I told you there's a solution? A kind of universal adapter for AI? That's where the Model Context Protocol, or MCP, comes in. Think of it as the "USB-C for AI." It's a game-changing open standard that lets your AI models talk to any external tool, database, or API, no matter what programming language they're built in.
In this guide, we're going to do a deep dive into MCP. We'll cover what it is, why it's such a big deal, & most importantly, how you can build your own MCP servers that work seamlessly across different programming languages. Let's get into it.
So, What Exactly IS the Model Context Protocol (MCP)?
At its heart, MCP is an open standard designed to solve the AI interoperability problem. For a long time, connecting an AI model (like the one powering ChatGPT) to an external tool (like a weather API or your company's CRM) required custom-built, one-off integrations. This was slow, expensive, & just not scalable.
MCP changes all that by creating a standardized way for AI systems & external tools to communicate. It works on a simple but powerful client-server architecture:
The MCP Host: This is the environment where the AI model lives, like your desktop or an IDE.
The MCP Client: This is the AI model itself, which acts as the "client," sending requests to the server.
The MCP Server: This is the external tool, data source, or API that you want your AI to connect to. It "serves" up data & functionality to the AI client.
An MCP server can expose three main things to an AI client:
Tools: These are functions that the AI can call to perform actions, like
1
get_weather_forecast
or
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search_customer_database
.
Resources: This is file-like data that the AI can read, like the contents of a webpage or a response from an API.
Prompts: These are pre-written templates that can help users accomplish specific tasks with the AI.
By standardizing this interaction, MCP makes it possible for any MCP-compliant AI client to connect to any MCP-compliant server, regardless of the underlying technology. Pretty cool, right?
Why MCP is a Game-Changer for Developers
You might be thinking, "Okay, a standard is nice, but why should I care?" Well, here's the thing: MCP isn't just a minor improvement. It's a fundamental shift in how we build AI-powered applications.
Before MCP, if you wanted your AI to use a specific tool, you had a few not-so-great options:
Platform-Specific Plugins: Think OpenAI's original plugins. They were a good start, but they locked you into a single ecosystem. Not very interoperable.
Frameworks like LangChain: LangChain is an awesome tool, but it's a framework, not a protocol. You still had to do a lot of custom work for each integration.
Custom APIs: This is the old-school way. It gives you a lot of control, but it's a ton of work, & you have to reinvent the wheel for every new AI model or tool you want to integrate.
MCP offers a much better way. It's open, reusable, & scalable. You can build a tool once as an MCP server, & any AI that speaks MCP can use it. This is HUGE.
And the best part? The industry is getting behind it in a big way. Major players like OpenAI, Microsoft, & Anthropic are all backing MCP, which means it's here to stay. This isn't some fleeting trend; it's the future of AI integration.
The Core Concepts of Building an MCP Server
Alright, let's get into the nitty-gritty. How do you actually build one of these things? The beauty of MCP is that it's designed to be language-agnostic. The key is to follow the protocol. Here are the core concepts you need to understand.
Choosing Your Language & Tools
You can build an MCP server in pretty much any language you want. Python & Node.js are popular choices because of their strong communities & great libraries, but you can also use Java, C#, Kotlin, or TypeScript.
The good news is that you don't have to start from scratch. There are official SDKs for many of these languages that handle all the low-level protocol stuff for you. This lets you focus on what's important: building your tool's functionality.
Data Serialization: The Universal Language
So, how do a client written in Python & a server written in Java talk to each other? The secret is a standardized data format called JSON-RPC 2.0.
JSON-RPC is a lightweight remote procedure call (RPC) protocol that uses JSON (JavaScript Object Notation) to structure its data. Because pretty much every programming language can understand JSON, it acts as a universal translator. The client sends a JSON-RPC request to the server, & the server sends a JSON-RPC response back. As long as both sides are speaking JSON-RPC, they don't need to know anything about each other's internal workings.
Transport Layers: How the Messages Travel
The JSON-RPC messages have to travel between the client & server somehow. MCP defines two main ways to do this, called transport layers:
Standard I/O (stdio): This is the simplest transport & is perfect for local development. The server reads requests from its standard input & writes responses to its standard output. It's fast, easy, & great for testing things on your own machine.
HTTP/SSE (Server-Sent Events): When you're ready to deploy your server so that remote clients can access it, you'll want to use an HTTP-based transport. The latest version of MCP uses a streamable HTTP transport, which allows for real-time, bidirectional communication between the client & server.
Defining Your Tools
This is where the magic happens. Your server's main job is to define a set of tools that the AI client can use. If you're building a server that needs to work with deep research models like those in ChatGPT, you'll need to implement at least two specific tools:
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search
: This tool takes a query from the AI & returns a list of potentially relevant results from your data source.
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fetch
: This tool takes an ID of a search result & retrieves the full content of that item.
Of course, you can define any custom tools you want. The key is to clearly document what each tool does, what parameters it expects, & what it returns.
A High-Level Guide to Creating Your First MCP Server
I won't write out a full code tutorial here because the exact syntax will depend on your chosen language & SDK. However, the conceptual steps are pretty much the same across the board.
Step 1: Set Up Your Project
First things first, you'll need to create a new project & install the official MCP SDK for your language. This will give you all the building blocks you need to create your server.
Step 2: Define Your Tool's Logic
This is the core of your server. Write the functions that will actually do the work. For example, if you're building a weather server, you'll need a function that takes a city name as input, calls a weather API, & returns the forecast.
Step 3: Create the Server Instance
Using the MCP SDK, you'll create an instance of an
1
McpServer
. You'll usually give it a name & a version number.
Step 4: Register Your Tools
Next, you need to tell the server about the tools you've created. The SDK will provide a way to register your functions as tools, making them available to any client that connects to your server.
Step 5: Choose a Transport & Run the Server
Finally, you'll decide whether you want to run your server locally with the
1
StdioServerTransport
or make it available over the network with an HTTP transport. Once you've chosen your transport, you'll connect it to your server instance & start it up. And that's it! Your MCP server is now running & ready to accept connections.
The Real Secret to Interoperability
Okay, so we've talked a lot about protocols & standards. But how does this all come together to create a server that works with any programming language?
Here's the key takeaway: Because MCP is a protocol, not an implementation.
Think about how the web works. You can use any web browser (Chrome, Firefox, Safari) to visit any website, right? You don't have to worry about whether the website's server is running on Apache (which is written in C) or Nginx (also C) or if the backend logic is written in Python, PHP, or Java. The browser doesn't care. All it cares about is that the server speaks HTTP.
MCP works in exactly the same way. As long as your server correctly implements the Model Context Protocol – meaning it communicates using JSON-RPC 2.0, exposes its tools in the standard way, & uses a supported transport layer – any MCP-compliant client can connect to it & use its tools. The client doesn't need to know, or care, that you wrote your server in Rust while the client itself is part of a C# application. They both speak the universal language of MCP.
This is what makes MCP so incredibly powerful. It abstracts away the implementation details & allows for true interoperability. It's the bridge that connects the entire AI ecosystem.
What About Customer-Facing AI?
MCP is FANTASTIC for connecting AI models to backend tools & data sources. But what if your goal is to build a sophisticated, conversational AI for your website or business? While you could technically use MCP to build a chatbot, it's often more practical to use a specialized platform.
Here's the thing, building a production-ready chatbot involves a lot more than just tool integration. You need to think about conversation management, user interface, analytics, & seamless deployment. This is where a platform like Arsturn comes into the picture.
Arsturn is a no-code platform that lets businesses create custom AI chatbots trained on their own data. If you want to provide instant customer support, answer frequently asked questions, or engage with your website visitors 24/7, Arsturn is an incredible solution. It's designed specifically for building & deploying these kinds of conversational AI experiences, handling all the complexity for you. So, while MCP is the perfect tool for developers looking to integrate AI with backend systems, Arsturn is the ideal solution for businesses looking to build powerful, customer-facing chatbots that can boost conversions & provide personalized experiences.
Best Practices & Things to Keep in Mind
As you start building your own MCP servers, here are a few final tips to keep in mind:
Be Careful with Logging: If you're using the stdio transport for local development, you have to be VERY careful not to write anything to standard output (
1
print()
,
1
console.log()
, etc.). This will corrupt the JSON-RPC messages & break your server. Always use a logging library that writes to standard error (stderr) or a file.
Think About Authentication: If your server is going to be exposed to the internet, you'll need to think about how to secure it. The latest version of MCP includes a robust OAuth 2.1-based authorization framework to help with this.
Test, Test, Test: There are some great debugging tools out there, like the MCP Inspector, that can help you test your server & make sure it's behaving as expected. Use them!
Wrapping It Up
The rise of the Model Context Protocol is honestly one of the most exciting things to happen in AI development in a long time. By providing a true open standard for interoperability, MCP is breaking down the walls between different AI ecosystems & paving the way for a new generation of powerful, interconnected AI applications.
Building an MCP server that can work with any programming language isn't about knowing some secret coding trick. It's about understanding & embracing the power of the protocol. By focusing on the standard – using JSON-RPC, defining your tools correctly, & choosing the right transport – you can create servers that are truly universal.
I hope this was helpful & gave you a solid starting point for your own MCP journey. Let me know what you think, & go build something awesome!