Unlocking Scalable AI: A Deep Dive into Model Context Protocol (MCP)

Deeper Dive: Creating Your Own MCP Servers and Transport Protocols

Over the past few days, we've journeyed through the essentials of the Model Context Protocol (MCP). We started by identifying the critical bottleneck in AI integration, introduced MCP as the "USB-C for LLMs," deconstructed its core components (Host, Client, Server), and then unveiled the seamless communication blueprint that enables LLMs to interact with external tools. Yesterday, we even touched upon practical implementation, showcasing how frameworks like LangChain can leverage pre-existing MCP servers to build powerful AI assistants.

But what if the tool you need isn't already exposed via an MCP Server? What if you have a proprietary internal system, a custom database, or a niche service that you want your LLM to interact with? This is where the true power and flexibility of MCP shine: you can create your own MCP Servers. Today, we'll take an even deeper dive, exploring how to build custom MCP Servers and understand the crucial role of different transport protocols.

Building from Scratch: Your Custom MCP Server

The ability to create your own MCP Server transforms MCP from a protocol you consume to a protocol you contribute to. This is particularly empowering for enterprises and developers in a vibrant tech ecosystem like Hyderabad, who often work with unique internal systems that AI could profoundly enhance.

The process involves defining your "tools" within the server – these are the specific functionalities you want to expose to your LLM. Let's consider a simple example using fast_mcp, a library designed to streamline MCP Server development:

Imagine you want to expose basic arithmetic functions to your LLM, or perhaps a custom internal weather lookup for your office building's sensor data:

from fast_mcp.tool_server import ToolServer, tool
import uvicorn # Used for HTTP server, not strictly needed for just defining tools

# Define your tools using the @tool decorator
class MyCustomTools:
    @tool()
    def add(self, a: float, b: float) -> float:
        """Adds two numbers."""
        return a + b

    @tool()
    def multiply(self, a: float, b: float) -> float:
        """Multiplies two numbers."""
        return a * b

    @tool()
    def get_local_office_weather(self, city: str = "Hyderabad") -> str:
        """
        Retrieves the current weather for a specified city, defaulting to Hyderabad.
        Example: 'The temperature in Hyderabad is 30C, partly cloudy.'
        """
        if city.lower() == "hyderabad":
            return "The temperature in Hyderabad is 30C, partly cloudy with 70% humidity."
        else:
            return f"Weather data for {city} not available."

# Create a ToolServer instance, passing in your tools.
# This object can then be configured to run with different transports.
tool_server = ToolServer(tools=[MyCustomTools()])

# Example of how you would run this via HTTP (Conceptual, not meant for direct execution in article)
# if __name__ == "__main__":
#     # This part would typically be in a separate script for your HTTP server setup
#     uvicorn.run(tool_server.app, host="0.0.0.0", port=8000)        

In this example, the add, multiply, and get_local_office_weather functions become callable "tools" for your LLM. The fast_mcp library handles the necessary MCP serialization, ensuring that your tool's description and parameters are correctly advertised to any MCP Client. This means your LLM (via LangChain, for instance) can now intelligently decide to "add two numbers" or "get the weather in Hyderabad" just by understanding the tool's description.

Transport Protocols: How MCP Servers Communicate

MCP is a protocol, but it needs an underlying transport mechanism to send and receive messages. Crucially, MCP is designed to be transport-agnostic, offering flexibility depending on your deployment needs. The two primary transport protocols for MCP server communication are:

1. STDIO (Standard Input/Output):
2. Streamable HTTP:

Seamless Client-Server Interaction Across Protocols

One of MCP's standout strengths is that a single LangChain-based client (within your MCP Host) can seamlessly interact with both STDIO and HTTP-based MCP Servers simultaneously. Your MCP Client configuration would simply specify the type of transport for each server, and the underlying MCP libraries handle the rest:

[
  {
    "name": "LocalMathServer",
    "type": "stdio",
    "command": ["python", "path/to/my_local_math_server.py"], # Command to run the STDIO server process
    "description": "Local server for basic math operations and quick tests."
  },
  {
    "name": "ProdBusinessAPI",
    "type": "http",
    "url": "https://api.yourcompany.com/mcp_business_data",
    "description": "Production server for accessing key business data and reports."
  },
  {
    "name": "IoTWeatherSensor",
    "type": "http",
    "url": "http://192.168.1.100:8080/mcp_weather", # Example of a local network IoT device
    "description": "Local network server for office weather sensor data."
  }
]        

This flexibility is incredibly powerful. It allows developers to test new tools locally using the simplicity of STDIO, iterate quickly, and then seamlessly deploy those same tools (or similar ones) as HTTP services in a production environment, all without fundamentally changing the AI agent's logic. It's a testament to MCP's design, which truly emphasizes modularity and ease of adoption for developers, whether they're working in a bustling startup in Hyderabad or a multinational corporation.

By understanding how to define your own tools and select the appropriate transport protocol, you gain the ultimate control over how your LLMs interact with any system, internal or external. This paves the way for truly bespoke and powerful AI applications tailored to your specific business needs. This deep dive solidifies MCP's position not just as a standard for connection, but as a framework for extending the reach of AI into every corner of your digital landscape.

What proprietary or internal system in your organization could be transformed by exposing its functionalities as an MCP Server, and which transport protocol (STDIO or HTTP) would be most suitable for it? Share your ideas below!

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