Making AI Integration Easier with Model Context Protocol (MCP)

When managing cloud environments, such as provisioning infrastructure with AWS EC2 instances or GCP Compute Engine, automating CI/CD workflows using Terraform to deploy microservices on Kubernetes clusters, or integrating Jenkins for continuous integration, connecting AI models to external systems like data lakes or API-based storage solutions can be a challenge. For example, in a microservices architecture, a common requirement is to link an AI-powered anomaly detection model with a monitoring system API or integrate a real-time data pipeline with a NoSQL database like MongoDB. Each service typically has its own API, authentication methods, and communication protocols, which can make integration time-consuming and error-prone.

Model Context Protocol (MCP) simplifies this by providing a standardized framework that allows AI models to interact with external systems. Instead of building separate connections for each service, MCP enables real-time, bidirectional communication between systems, ensuring smooth data flow across different environments.

This post will explain what MCP is, how it simplifies AI model integrations, and guide you through setting up an MCP server for tool integration in a practical development scenario.

What is Model Context Protocol (MCP)?

MCP is a communication protocol designed to simplify the integration between AI models and external systems, such as APIs, cloud services, and tools. Unlike traditional integrations that often require separate codebases, custom authentication methods, and individual handling of errors and data formats for each API, MCP unifies these complexities into a single, standardized communication layer. This reduces the overhead of maintaining multiple connections, handling different security protocols, and managing various data exchange formats, allowing for more efficient integration across platforms like REST APIs, message queues (e.g., Kafka), and cloud-based data storage solutions (e.g., AWS S3 or GCP Cloud Storage).

What Makes MCP Stand Out? 

MCP excels in simplifying the integration of AI models with external systems by offering key features that enhance efficiency, security, and manageability.

• Real-time communication: MCP facilitates real-time, dynamic interactions between AI models and external services.
• Unified interface: It provides a single interface to interact with various tools and services, reducing the complexity of managing multiple APIs.
• Security and control: MCP ensures consistent security standards across all integrations.

To visualize it, think of MCP as a hub in a network that connects various devices and systems. Just as a network hub efficiently routes data between multiple computers without requiring complex setups on each device, MCP simplifies communication between AI models and external systems, enabling them to work together seamlessly without the need for custom configurations.

Kubiya's Use of MCP

One of the ways MCP enhances AI model integration is through platforms like Kubiya. Kubiya uses MCP to simply integrate AI models with local applications, such as Claude Desktop and Cursor IDE. With the Kubiya CLI, developers can easily configure their API key and teammate context, allowing local applications to access relevant team information in real-time. This integration significantly streamlines the workflow, making it easier for AI models to interact with various tools and external systems.

For more details on how Kubiya uses MCP, you can explore their integration here.

Why Should Developers Use MCP?

MCP provides a simple solution to the common challenges developers face when integrating AI models with external services.

Connecting AI Models to External APIs and Services

In most AI-driven systems, external services like databases or third-party APIs are required for data retrieval or triggering actions. Without MCP, developers would need to build separate integrations for each service, leading to unnecessary complexity and maintenance work. MCP reduces that by offering a single communication protocol for all interactions, making the entire process more efficient and manageable.

Simplifying Tool Integration in Development Environments

Development environments like Cursor or VS Code require frequent integrations with various tools. MCP simplifies these interactions by providing one common protocol, saving developers from the burden of managing multiple API connections. By using MCP, developers can focus on their work rather than spending time maintaining connections to different services.

Enabling Real-time Communication Between Microservices

In cloud-native applications, communication between microservices is critical, and MCP ensures that such communication is handled in real-time. This is especially valuable for applications where multiple services need to share and process information instantly.

Building Context-Aware Applications

MCP is ideal for building context-aware applications that dynamically fetch and process data based on user input or environmental changes. Whether it’s chatbots, virtual assistants, or other intelligent systems, MCP allows AI models to access up-to-date information and respond accordingly, making these applications smarter and more responsive.

Setting Up an MCP Server for Tool Integration

Now that we've seen the core concepts of MCP, let’s walk through setting up an MCP server. We'll build a simple server that exposes basic math operations.

Step 1: Set Up Your Environment

First, create a directory for your project and set up a virtual environment:

mkdir mcp-server && cd mcp-server
python3 -m venv .venv
source .venv/bin/activate
# Install MCP and CLI tools
pip install mcp mcp[cli]

Step 2: Create the Server Code

Create a Python file named calculator.py and define tools for basic math operations (addition, subtraction, multiplication, division):

from mcp.server.fastmcp import FastMCP
import math
# Create a new MCP server instance
mcp = FastMCP("Calculator MCP")
# Define MCP tools (math operations)
@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b
@mcp.tool()
def subtract(a: int, b: int) -> int:
    """Subtract two numbers"""
    return a - b
@mcp.tool()
def multiply(a: int, b: int) -> int:
    """Multiply two numbers"""
    return a * b
@mcp.tool()
def divide(a: int, b: int) -> float:
    """Divide two numbers"""
    return a / b
# Start the MCP server with STDIO transport
if __name__ == "__main__":
    mcp.run(transport="stdio")

Step 3: Run the Server

Run the server using the following command:

python3 calculator.py

This will start the server, and it will begin listening for requests.

Step 4: Test the Server Using MCP Inspector

Once the server is running, you can test it using MCP Inspector. First, open MCP Inspector:

npx @modelcontextprotocol/inspector

In the Command field, enter python3, and in the Arguments field, enter calculator.py. Then, click Connect to start the connection.

This image shows MCP Inspector, where you can set up and test the server connection.

Step 5: Interact with the Tools and Resources

Once connected, you can interact with the exposed tools and resources:

Now, go to the Tools tab in MCP Inspector, click List Tools, and select the math operation you want to use (e.g., add). Enter some test values and click Run Tool to get the result.

Here, we test the "add" tool by entering two numbers, 20 and 30, to calculate the sum.

Go to the Resources tab, click List Resources

In this image, we can see the multiplication operation being tested by entering two numbers.

This is a basic setup to get a feel for what MCP can do. With this setup, you can:

• Add more tools or integrate additional services to enhance functionality.
• Experiment with other transport types (like SSE) to connect MCP with different development environments like Cursor or Claude.
• Scale this example to a more complex, production-ready system for handling advanced integrations.

Conclusion

MCP is a powerful protocol that streamlines the process of integrating AI models with external tools, APIs, and services. This post provided an overview of MCP, explored its key use cases, and guided you through setting up an MCP server for tool and resource integration.

Whether you’re working with APIs, microservices, or context-aware applications, MCP simplifies the process, allowing you to focus on building intelligent systems rather than dealing with complex integrations.

The future of AI integrations is here with MCP, and it’s easier than ever to get started.

‍