Artificial Intelligence is evolving rapidly, and one of the most pressing challenges is enabling AI models to interact effectively with external tools, data sources, and APIs. The Model Context Protocol (MCP) solves this problem by acting as a bridge between AI models and external services, creating a standardized communication framework that enhances tool integration, accessibility, and AI reasoning capabilities.

What is Model Context Protocol (MCP)?

MCP is a protocol designed to enable AI models, such as Azure OpenAI models, to interact seamlessly with external tools and services. Think of MCP as a universal USB-C connector for AI, allowing language models to fetch information, interact with APIs, and execute tasks beyond their built-in knowledge.

 Key Features of MCP

\n
• Standardized Communication – MCP provides a structured way for AI models to interact with various tools.
\n
• Tool Access & Expansion – AI assistants can now utilize external tools for real-time insights.
\n
• Secure & Scalable – Enables safe and scalable integration with enterprise applications.
\n
• Multi-Modal Integration – Supports STDIO, SSE (Server-Sent Events), and WebSocket communication methods.
\n

MCP Architecture & How It Works

MCP follows a client-server architecture that allows AI models to interact with external tools efficiently. Here’s how it works:

Components of MCP

\n
1. MCP Host – The AI model (e.g., Azure OpenAI GPT) requesting data or actions.
\n
2. MCP Client – An intermediary service that forwards the AI model's requests to MCP servers.
\n
3. MCP Server – Lightweight applications that expose specific capabilities (APIs, databases, files, etc.).
\n
4. Data Sources – Various backend systems, including local storage, cloud databases, and external APIs.
\n

 Data Flow in MCP

\n
1. The AI model sends a request (e.g., \"fetch user profile data\").
\n
2. The MCP client forwards the request to the appropriate MCP server.
\n
3. The MCP server retrieves the required data from a database or API.
\n
4. The response is sent back to the AI model via the MCP client.
\n

Integrating MCP with Azure OpenAI Services

Microsoft has integrated MCP with Azure OpenAI Services, allowing GPT models to interact with external services and fetch live data. This means AI models are no longer limited to static knowledge but can access real-time information.

Benefits of Azure OpenAI Services + MCP Integration

✔ Real-time Data Fetching – AI assistants can retrieve fresh information from APIs, databases, and internal systems.

✔ Contextual AI Responses – Enhances AI responses by providing accurate, up-to-date information.

✔ Enterprise-Ready – Secure and scalable for business applications, including finance, healthcare, and retail.

Hands-On Tools for MCP Implementation

To implement MCP effectively, Microsoft provides two powerful tools: Semantic Workbench and AI Gateway.

Microsoft Semantic Workbench

A development environment for prototyping AI-powered assistants and integrating MCP-based functionalities.

 Features:

\n
• Build and test multi-agent AI assistants.
\n
• Configure settings and interactions between AI models and external tools.
\n
• Supports GitHub Codespaces for cloud-based development.
\n

Explore Semantic Workbench

Workbench interface examples

Microsoft AI Gateway

A plug-and-play interface that allows developers to experiment with MCP using Azure API Management.

Features:

\n
• Credential Manager – Securely handle API credentials.
\n
• Live Experimentation – Test AI model interactions with external tools.
\n
• Pre-built Labs – Hands-on learning for developers.
\n

Explore AI Gateway

Setting Up MCP with Azure OpenAI Services

Step 1: Create a Virtual Environment

First, create a virtual environment using Python:

python -m venv .venv

Activate the environment:

# Windows

venv\\Scripts\\activate

# MacOS/Linux

source .venv/bin/activate

Step 2: Install Required Libraries

Create a requirements.txt file and add the following dependencies:

langchain-mcp-adapters\n\nlanggraph\n\nlangchain-openai

Then, install the required libraries:

pip install -r requirements.txt

Step 3: Set Up OpenAI API Key

Ensure you have your OpenAI API key set up:

# Windows

setx OPENAI_API_KEY \"<your_api_key>

# MacOS/Linux

export OPENAI_API_KEY=<your_api_key>

Building an MCP Server

This server performs basic mathematical operations like addition and multiplication.

Create the Server File

First, create a new Python file:

touch math_server.py

Then, implement the server:

from mcp.server.fastmcp import FastMCP

# Initialize the server\n\nmcp = FastMCP(\"Math\")\n\n\n\nMCP.tool()\n\ndef add(a: int, b: int) -> int:\n\n    return a + b\n\n\n\nMCP.tool()\n\ndef multiply(a: int, b: int) -> int:\n\n    return a * b\n\n\n\nif __name__ == \"__main__\":\n\n    mcp.run(transport=\"stdio\")

Your MCP server is now ready to run.

 Building an MCP Client

This client connects to the MCP server and interacts with it.

Create the Client File

First, create a new file:

touch client.py

Then, implement the client:

import asyncio\n\nfrom mcp import ClientSession, StdioServerParameters\n\nfrom langchain_openai import ChatOpenAI\n\nfrom mcp.client.stdio import stdio_client\n\n\n\n# Define server parameters\n\nserver_params = StdioServerParameters(\n\n    command=\"python\",\n\n    args=[\"math_server.py\"],\n\n)\n\n\n\n# Define the model\n\nmodel = ChatOpenAI(model=\"gpt-4o\")\n\n\n\nasync def run_agent():\n\n    async with stdio_client(server_params) as (read, write):\n\n        async with ClientSession(read, write) as session:\n\n            await session.initialize()\n\n            tools = await load_mcp_tools(session)\n\n            agent = create_react_agent(model, tools)\n\n            agent_response = await agent.ainvoke({\"messages\": \"what's (4 + 6) x 14?\"})\n\n            return agent_response[\"messages\"][3].content\n\n\n\nif __name__ == \"__main__\":\n\n    result = asyncio.run(run_agent())\n\n    print(result)

Your client is now set up and ready to interact with the MCP server.

Running the MCP Server and Client

Step 1: Start the MCP Server

Open a terminal and run:

python math_server.py

This starts the MCP server, making it available for client connections.

Step 2: Run the MCP Client

In another terminal, run:

python client.py

Expected Output

140

This means the AI agent correctly computed (4 + 6) x 14 using both the MCP server and GPT-4o.

Conclusion

Integrating MCP with Azure OpenAI Services enables AI applications to securely interact with external tools, enhancing functionality beyond text-based responses. With standardized communication and improved AI capabilities, developers can build smarter and more interactive AI-powered solutions. By following this guide, you can set up an MCP server and client, unlocking the full potential of AI with structured external interactions.

Next Steps:

\n
• Explore more MCP tools and integrations.
\n
• Extend your MCP setup to work with additional APIs.
\n
• Deploy your solution in a cloud environment for broader accessibility.
\n

For further details, visit the GitHub repository for MCP integration examples and best practices.

\n
• MCP GitHub Repository
\n
• MCP Documentation
\n
• Semantic Workbench
\n
• AI Gateway
\n
• MCP Video Walkthrough
\n
• MCP Blog
\n
• MCP Github End to End Demo
\n

Artificial Intelligence is evolving rapidly, and one of the most pressing challenges is enabling AI models to interact effectively with external tools, data sources, and APIs. The Model Context Protocol (MCP) solves this problem by acting as a bridge between AI models and external services, creating a standardized communication framework that enhances tool integration, accessibility, and AI reasoning capabilities.

What is Model Context Protocol (MCP)?

MCP is a protocol designed to enable AI models, such as Azure OpenAI models, to interact seamlessly with external tools and services. Think of MCP as a universal USB-C connector for AI, allowing language models to fetch information, interact with APIs, and execute tasks beyond their built-in knowledge.

 Key Features of MCP

\n
• Standardized Communication – MCP provides a structured way for AI models to interact with various tools.
\n
• Tool Access & Expansion – AI assistants can now utilize external tools for real-time insights.
\n
• Secure & Scalable – Enables safe and scalable integration with enterprise applications.
\n
• Multi-Modal Integration – Supports STDIO, SSE (Server-Sent Events), and WebSocket communication methods.
\n

MCP Architecture & How It Works

MCP follows a client-server architecture that allows AI models to interact with external tools efficiently. Here’s how it works:

Components of MCP

\n
1. MCP Host – The AI model (e.g., Azure OpenAI GPT) requesting data or actions.
\n
2. MCP Client – An intermediary service that forwards the AI model's requests to MCP servers.
\n
3. MCP Server – Lightweight applications that expose specific capabilities (APIs, databases, files, etc.).
\n
4. Data Sources – Various backend systems, including local storage, cloud databases, and external APIs.
\n

 Data Flow in MCP

\n
1. The AI model sends a request (e.g., \"fetch user profile data\").
\n
2. The MCP client forwards the request to the appropriate MCP server.
\n
3. The MCP server retrieves the required data from a database or API.
\n
4. The response is sent back to the AI model via the MCP client.
\n

Integrating MCP with Azure OpenAI Services

Microsoft has integrated MCP with Azure OpenAI Services, allowing GPT models to interact with external services and fetch live data. This means AI models are no longer limited to static knowledge but can access real-time information.

Benefits of Azure OpenAI Services + MCP Integration

✔ Real-time Data Fetching – AI assistants can retrieve fresh information from APIs, databases, and internal systems.

✔ Contextual AI Responses – Enhances AI responses by providing accurate, up-to-date information.

✔ Enterprise-Ready – Secure and scalable for business applications, including finance, healthcare, and retail.

Hands-On Tools for MCP Implementation

To implement MCP effectively, Microsoft provides two powerful tools: Semantic Workbench and AI Gateway.

Microsoft Semantic Workbench

A development environment for prototyping AI-powered assistants and integrating MCP-based functionalities.

 Features:

\n
• Build and test multi-agent AI assistants.
\n
• Configure settings and interactions between AI models and external tools.
\n
• Supports GitHub Codespaces for cloud-based development.
\n

Explore Semantic Workbench

Workbench interface examples

Microsoft AI Gateway

A plug-and-play interface that allows developers to experiment with MCP using Azure API Management.

Features:

\n
• Credential Manager – Securely handle API credentials.
\n
• Live Experimentation – Test AI model interactions with external tools.
\n
• Pre-built Labs – Hands-on learning for developers.
\n

Explore AI Gateway

Setting Up MCP with Azure OpenAI Services

Step 1: Create a Virtual Environment

First, create a virtual environment using Python:

Activate the environment:

# Windows

# MacOS/Linux

Step 2: Install Required Libraries

Create a requirements.txt file and add the following dependencies:

Then, install the required libraries:

Step 3: Set Up OpenAI API Key

Ensure you have your OpenAI API key set up:

# Windows

# MacOS/Linux

Building an MCP Server

This server performs basic mathematical operations like addition and multiplication.

Create the Server File

First, create a new Python file:

Then, implement the server:

Your MCP server is now ready to run.

 Building an MCP Client

This client connects to the MCP server and interacts with it.

Create the Client File

First, create a new file:

Then, implement the client:

Your client is now set up and ready to interact with the MCP server.

Running the MCP Server and Client

Step 1: Start the MCP Server

Open a terminal and run:

This starts the MCP server, making it available for client connections.

Step 2: Run the MCP Client

In another terminal, run:

Expected Output

140

This means the AI agent correctly computed (4 + 6) x 14 using both the MCP server and GPT-4o.

Conclusion

Integrating MCP with Azure OpenAI Services enables AI applications to securely interact with external tools, enhancing functionality beyond text-based responses. With standardized communication and improved AI capabilities, developers can build smarter and more interactive AI-powered solutions. By following this guide, you can set up an MCP server and client, unlocking the full potential of AI with structured external interactions.

Next Steps:

\n
• Explore more MCP tools and integrations.
\n
• Extend your MCP setup to work with additional APIs.
\n
• Deploy your solution in a cloud environment for broader accessibility.
\n

For further details, visit the GitHub repository for MCP integration examples and best practices.

\n
• MCP GitHub Repository
\n
• MCP Documentation
\n
• Semantic Workbench
\n
• AI Gateway
\n
• MCP Video Walkthrough
\n
• MCP Blog
\n
• MCP Github End to End Demo
\n

In the Server example, you are using `MCP.tool()`. I believe this should be `@mcp.tool()`.

If you don't have the `@` symbol, the code will run, but the tools are not exposes as tools from the server so it doesn't actually work correctly. It's hard to spot, initially, as the LLM will work out the result - the mcp tools will just never be called :)

You're right  flytzen without @mcp.tool(), the function isn't registered as a tool, so it won’t actually get called even though the LLM may return a result. Thanks for pointing it out.

Amazing article Sharda_Kaur ! 
My 2 cents for .NET devs, check these resources: 

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• 📖 MCP C# SDK: https://github.com/modelcontextprotoc...
\n
• 🚀 Sample Code: https://aka.ms/aspiremcp/repo
\n
• 📎 Generative AI .Net: https://aka.ms/genainet
\n
• 🎦 5 min video overview: https://www.youtube.com/watch?v=2holzbob1_I
\n

Best!

Thank you elbruno for sharing these resources, I appreciate that !