4/24/2025

Creating MCP Servers: A Detailed Review of Available Tools

Creating MCP (Model Context Protocol) servers is becoming an essential talent for developers looking to harness the immense power of AI models effectively. With the expanding versatility of applications that utilize AI, those who understand how to deploy MCP servers can significantly augment their workflows and improve the efficiency of responses within various domains.

In this blog post, we'll explore the various tools available for creating MCP servers, the best practices for deploying them, and highlights from the Model Context Protocol documentation. So roll up your sleeves as we dive deep into the fascinating world of MCP servers!

What is an MCP Server?

To set the stage, an MCP server acts as a communication bridge between AI models and external resources. It allows developers to connect AI models securely to services and data sources such as APIs, databases, and more. This system fluidly integrates diverse capabilities like fetching real-time information or executing complex actions, which expands the functionality of AI tools.

Getting Started with MCP Servers

Creating MCP servers requires a clear understanding of the architecture and functionality of the Model Context Protocol. The essential components of MCP servers include:

Resources: Accessible data that clients can read from (like API responses).
Tools: Functions that can be executed with user approval, enabling more dynamic interactions with users.
Prompts: Pre-defined templates to help users accomplish specific tasks.

For developers new to building MCP servers, the Model Context Protocol Quickstart Guide is a useful resource. It outlines basic setup processes and introduces the robust tools available.

Available Tools for Creating MCP Servers

Let's examine some of the vital tools you can employ to create an MCP server. You'll be pleased to know that the MCP ecosystem already supports several development kits:

1. Python SDK

The Python SDK is a powerful tool for creating MCP servers with a familiar syntax for Python developers. By using the Python library, you can seamlessly integrate existing data and services into your applications without needing to write everything from scratch.

2. TypeScript SDK

For those who prefer working within the JavaScript ecosystem, the TypeScript SDK offers flexibility and type safety. This allows for a structured approach when creating complex applications while easily implementing features to interact with various data sources.

3. Java SDK

If your preference leans toward Java, check out the Java SDK. Java developers can now build adaptable MCP servers that integrate additional functionalities without a steep learning curve.

4. Kotlin SDK

The Kotlin SDK extends support for Kotlin developers, enabling streamlined creation of MCP servers that interface effectively with modern applications, particularly mobile apps.

5. C# SDK

Lastly, the C# SDK caters to .NET developers looking to enhance their applications with customized MCP servers. This SDK ensures that Windows-based applications can utilize the Model Context Protocol’s wizardry as well.

Step-by-Step: Building an MCP Server

Let’s take a closer look at building a simple MCP server with the Python SDK. We will make a weather server that leverages real-time data from the National Weather Service API.

Step 1: Environment Setup

Start by making sure you have Python 3.10 or higher installed on your machine. You can set up your environment with.

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curl -LsSf https://astral.sh/uv/install.sh | sh

Once installed, create a new project directory:

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uv init weather
cd weather

Initialize your virtual environment:

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source .venv/bin/activate

Step 2: Server File Creation

Create a server file named

weather.py

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touch weather.py

Step 3: Importing Libraries

weather.py

, bring in the required libraries as follows:

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from typing import Any
import httpx
from mcp.server.fastmcp import FastMCP

# Initialize FastMCP server
mcp = FastMCP("weather")

# Constants
NWS_API_BASE = "https://api.weather.gov"
USER_AGENT = "weather-app/1.0"

Step 4: Defining Helper Functions

We need to create two helper functions for fetching data from the National Weather Service API:

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async def make_nws_request(url: str) -> dict[str, Any] | None:
    headers = {"User-Agent": USER_AGENT, "Accept": "application/geo+json"}
    async with httpx.AsyncClient() as client:
        response = await client.get(url, headers=headers)
        return response.json() if response.status_code == 200 else None

def format_alert(feature: dict) -> str:
    props = feature["properties"]
    return f"""
    Event: {props.get('event', 'Unknown')}
    Area: {props.get('areaDesc', 'Unknown')}
    Severity: {props.get('severity', 'Unknown')}
    Description: {props.get('description', 'No description available')}
    Instructions: {props.get('instruction', 'No specific instructions provided')}
    """

Step 5: Implementing Tool Execution

Now, let’s add the tools that will fetch alerts and forecasts:

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@mcp.tool()
async def get_alerts(state: str) -> str:
    url = f"{NWS_API_BASE}/alerts/active/area/{state}"
    data = await make_nws_request(url)
    if data is None:
        return "Unable to fetch alerts."
    # Process alerts
@mcp.tool()
async def get_forecast(latitude: float, longitude: float) -> str:
    points_url = f"{NWS_API_BASE}/points/{latitude},{longitude}"
    points_data = await make_nws_request(points_url)
    if points_data is None:
        return "Unable to fetch forecast data."

Step 6: Running the Server

Finally, we need to run our server to bring it to life:

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if __name__ == '__main__':
    mcp.run(transport='stdio')

The server can now be initiated, and you can test it through any available MCP host, such as the Claude Desktop.

Best Practices for MCP Server Development

As you embark on creating MCP servers, consider adhering to the following best practices:

Maintain Clear API Documentation: Always make sure your APIs are well documented for easier integration.
Version Control: Keep track of any changes that might affect the API or server functionalities using Git.
Implement Security Measures: Ensure that tools validate inputs and offer authentication mechanisms.
Testing: Continuously test on various scenarios to ensure robust performance.

Conclusion

Creating an MCP server is not just a technical skill but an art form that allows you to connect AI with real-world functionality. The tools available make it easier than ever to set up robust connections between AI models and external data sources.

With Arsturn, you can take your journey to the next level, utilizing AI chatbots to engage your audience before. Whether you're a small business, a start-up, or a giant corporation, Arsturn provides an effortless solution to integrate and build meaningful connections through conversational AI. You can get started without any credit cards required at Arsturn.com.

Take hold of the powerful capabilities with MCP servers & create experiences that connect with users meaningfully!

Now, as we sail into the future of AI, the tools to create MCP servers will continue evolving. Keep your code fresh, learn continuously, and never stop innovating!