Unleashing the Power of Model Context Protocol (MCP): A Game-Changer in AI Integration
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 Standardized Communication – MCP provides a structured way for AI models to interact with various tools. Tool Access & Expansion – AI assistants can now utilize external tools for real-time insights. Secure & Scalable – Enables safe and scalable integration with enterprise applications. Multi-Modal Integration – Supports STDIO, SSE (Server-Sent Events), and WebSocket communication methods. 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 MCP Host – The AI model (e.g., Azure OpenAI GPT) requesting data or actions. MCP Client – An intermediary service that forwards the AI model's requests to MCP servers. MCP Server – Lightweight applications that expose specific capabilities (APIs, databases, files, etc.). Data Sources – Various backend systems, including local storage, cloud databases, and external APIs. Data Flow in MCP The AI model sends a request (e.g., "fetch user profile data"). The MCP client forwards the request to the appropriate MCP server. The MCP server retrieves the required data from a database or API. The response is sent back to the AI model via the MCP client. 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: Build and test multi-agent AI assistants. Configure settings and interactions between AI models and external tools. Supports GitHub Codespaces for cloud-based development. 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: Credential Manager – Securely handle API credentials. Live Experimentation – Test AI model interactions with external tools. Pre-built Labs – Hands-on learning for developers. 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 langgraph langchain-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 mcp = FastMCP("Math") MCP.tool() def add(a: int, b: int) -> int: return a + b MCP.tool() def multiply(a: int, b: int) -> int: return a * b if __name__ == "__main__": 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 from mcp import ClientSession, StdioServerParameters from langchain_openai import ChatOpenAI from mcp.client.stdio import stdio_client # Define server parameters server_params = StdioServerParameters( command="python", args=["math_server.py"], ) # Define the model model = ChatOpenAI(model="gpt-4o") async def run_agent(): async with stdio_client(server_params) as (read, write): async with ClientSession(read, write) as session: await session.initialize() tools = await load_mcp_tools(session) agent = create_react_agent(model, tools) agent_response = await agent.ainvoke({"messages": "what's (4 + 6) x 14?"}) return agent_response["messages"][3].content if __name__ == "__main__": result = asyncio.run(run_agent()) 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: Explore more MCP tools and integrations. Extend your MCP setup to work with additional APIs. Deploy your solution in a cloud environment for broader accessibility. For further details, visit the GitHub repository for MCP integration examples and best practices. MCP GitHub Repository MCP Documentation Semantic Workbench AI Gateway MCP Video Walkthrough MCP Blog MCP Github End to End DemoAzure OpenAI Service is now generally available
Early this week Satya Nadella, Microsoft CEO, and Eric Boyd, Corporate AI Vice President, announced Azure OpenAI Service generally available, which will soon include ChatGPT – the fine-tuned version of GPT-3.5 built upon Azure AI infrastructure gone viral in the last few weeks. But let’s take a step back. What is Azure OpenAI? And how can you get started?Como começar e crescer no mercado de tecnologia
A #JornadaTech é uma maratona de mentorias online do Microsoft Reactor São Paulo, sobre carreira na tecnologia, em que você poderá aprender mais sobre as áreas de Cloud, Segurança, Programação e Dados. Neste artigo, você encontrará algumas dicas e recursos para começar e crescer na carreira de tecnologia.Starting your Kaggle challenge using Azure Machine Learning Services
One of the main advantages of Azure ML is the ability to do hyperparameter optimization by scheduling experiments. So have you tried this this with dataset hosted on Kaggle? Kaggle has over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time. Kaggle does offers a no-setup, customizable, Jupyter Notebooks environment. Access GPUs at no cost to you and a huge repository of community published data & code. However, there are times when you want to build your experiment using Azure and an Azure ML workspace in the azure portal.Smart Auditing: Leveraging Azure AI Agents to Transform Financial Oversight
In today's data-driven business environment, audit teams often spend weeks poring over logs and databases to verify spending and billing information. This time-consuming process is ripe for automation. But is there a way to implement AI solutions without getting lost in complex technical frameworks? While tools like LangChain, Semantic Kernel, and AutoGen offer powerful AI agent capabilities, sometimes you need a straightforward solution that just works. So, what's the answer for teams seeking simplicity without sacrificing effectiveness? This tutorial will show you how to use Azure AI Agent Service to build an AI agent that can directly access your Postgres database to streamline audit workflows. No complex chains or graphs required, just a practical solution to get your audit process automated quickly. The Auditing Challenge: It's the month end, and your audit team is drowning in spreadsheets. As auditors reviewing financial data across multiple SaaS tenants, you're tasked with verifying billing accuracy by tracking usage metrics like API calls, storage consumption, and user sessions in Postgres databases. Each tenant generates thousands of transactions daily, and traditionally, this verification process consumes weeks of your team's valuable time. Typically, teams spend weeks: Manually extracting data from multiple database tables. Cross-referencing usage with invoices. Investigating anomalies through tedious log analysis. Compiling findings into comprehensive reports. With an AI-powered audit agent, you can automate these tasks and transform the process. Your AI assistant can: Pull relevant usage data directly from your database Identify billing anomalies like unexpected usage spikes Generate natural language explanations of findings Create audit reports that highlight key concerns For example, when reviewing a tenant's invoice, your audit agent can query the database for relevant usage patterns, summarize anomalies, and offer explanations: "Tenant_456 experienced a 145% increase in API usage on April 30th, which explains the billing increase. This spike falls outside normal usage patterns and warrants further investigation." Let’s build an AI agent that connects to your Postgres database and transforms your audit process from manual effort to automated intelligence. Prerequisites: Before we start building our audit agent, you'll need: An Azure subscription (Create one for free). The Azure AI Developer RBAC role assigned to your account. Python 3.11.x installed on your development machine. OR You can also use GitHub Codespaces, which will automatically install all dependencies for you. You’ll need to create a GitHub account first if you don’t already have one. Setting Up Your Database: For this tutorial, we'll use Neon Serverless Postgres as our database. It's a fully managed, cloud-native Postgres solution that's free to start, scales automatically, and works excellently for AI agents that need to query data on demand. Creating a Neon Database on Azure: Open the Neon Resource page on the Azure portal Fill out the form with the required fields and deploy your database After creation, navigate to the Neon Serverless Postgres Organization service Click on the Portal URL to access the Neon Console Click "New Project" Choose an Azure region Name your project (e.g., "Audit Agent Database") Click "Create Project" Once your project is successfully created, copy the Neon connection string from the Connection Details widget on the Neon Dashboard. It will look like this: postgresql://[user]:[password]@[neon_hostname]/[dbname]?sslmode=require Note: Keep this connection string saved; we'll need it shortly. Creating an AI Foundry Project on Azure: Next, we'll set up the AI infrastructure to power our audit agent: Create a new hub and project in the Azure AI Foundry portal by following the guide. Deploy a model like GPT-4o to use with your agent. Make note of your Project connection string and Model Deployment name. You can find your connection string in the overview section of your project in the Azure AI Foundry portal, under Project details > Project connection string. Once you have all three values on hand: Neon connection string, Project connection string, and Model Deployment Name, you are ready to set up the Python project to create an Agent. All the code and sample data are available in this GitHub repository. You can clone or download the project. Project Environment Setup: Create a .env file with your credentials: PROJECT_CONNECTION_STRING="<Your AI Foundry connection string> "AZURE_OPENAI_DEPLOYMENT_NAME="gpt4o" NEON_DB_CONNECTION_STRING="<Your Neon connection string>" Create and activate a virtual environment: python -m venv .venv source .venv/bin/activate # on macOS/Linux .venv\Scripts\activate # on Windows Install required Python libraries: pip install -r requirements.txt Example requirements.txt: Pandas python-dotenv sqlalchemy psycopg2-binary azure-ai-projects ==1.0.0b7 azure-identity Load Sample Billing Usage Data: We will use a mock dataset for tenant usage, including computed percent change in API calls and storage usage in GB: tenant_id date api_calls storage_gb tenant_456 2025-04-01 1000 25.0 tenant_456 2025-03-31 950 24.8 tenant_456 2025-03-30 2200 26.0 Run python load_usage_data.py Python script to create and populate the usage_data table in your Neon Serverless Postgres instance: # load_usage_data.py file import os from dotenv import load_dotenv from sqlalchemy import ( create_engine, MetaData, Table, Column, String, Date, Integer, Numeric, ) # Load environment variables from .env load_dotenv() # Load connection string from environment variable NEON_DB_URL = os.getenv("NEON_DB_CONNECTION_STRING") engine = create_engine(NEON_DB_URL) # Define metadata and table schema metadata = MetaData() usage_data = Table( "usage_data", metadata, Column("tenant_id", String, primary_key=True), Column("date", Date, primary_key=True), Column("api_calls", Integer), Column("storage_gb", Numeric), ) # Create table with engine.begin() as conn: metadata.create_all(conn) # Insert mock data conn.execute( usage_data.insert(), [ { "tenant_id": "tenant_456", "date": "2025-03-27", "api_calls": 870, "storage_gb": 23.9, }, { "tenant_id": "tenant_456", "date": "2025-03-28", "api_calls": 880, "storage_gb": 24.0, }, { "tenant_id": "tenant_456", "date": "2025-03-29", "api_calls": 900, "storage_gb": 24.5, }, { "tenant_id": "tenant_456", "date": "2025-03-30", "api_calls": 2200, "storage_gb": 26.0, }, { "tenant_id": "tenant_456", "date": "2025-03-31", "api_calls": 950, "storage_gb": 24.8, }, { "tenant_id": "tenant_456", "date": "2025-04-01", "api_calls": 1000, "storage_gb": 25.0, }, ], ) print("✅ usage_data table created and mock data inserted.") Create a Postgres Tool for the Agent: Next, we configure an AI agent tool to retrieve data from Postgres. The Python script billing_agent_tools.py contains: The function billing_anomaly_summary() that: Pulls usage data from Neon. Computes % change in api_calls. Flags anomalies with a threshold of > 1.5x change. Exports user_functions list for the Azure AI Agent to use. You do not need to run it separately. # billing_agent_tools.py file import os import json import pandas as pd from sqlalchemy import create_engine from dotenv import load_dotenv # Load environment variables load_dotenv() # Set up the database engine NEON_DB_URL = os.getenv("NEON_DB_CONNECTION_STRING") db_engine = create_engine(NEON_DB_URL) # Define the billing anomaly detection function def billing_anomaly_summary( tenant_id: str, start_date: str = "2025-03-27", end_date: str = "2025-04-01", limit: int = 10, ) -> str: """ Fetches recent usage data for a SaaS tenant and detects potential billing anomalies. :param tenant_id: The tenant ID to analyze. :type tenant_id: str :param start_date: Start date for the usage window. :type start_date: str :param end_date: End date for the usage window. :type end_date: str :param limit: Maximum number of records to return. :type limit: int :return: A JSON string with usage records and anomaly flags. :rtype: str """ query = """ SELECT date, api_calls, storage_gb FROM usage_data WHERE tenant_id = %s AND date BETWEEN %s AND %s ORDER BY date DESC LIMIT %s; """ df = pd.read_sql(query, db_engine, params=(tenant_id, start_date, end_date, limit)) if df.empty: return json.dumps( {"message": "No usage data found for this tenant in the specified range."} ) df.sort_values("date", inplace=True) df["pct_change_api"] = df["api_calls"].pct_change() df["anomaly"] = df["pct_change_api"].abs() > 1.5 return df.to_json(orient="records") # Register this in a list to be used by FunctionTool user_functions = [billing_anomaly_summary] Create and Configure the AI Agent: Now we'll set up the AI agent and integrate it with our Neon Postgres tool using the Azure AI Agent Service SDK. The Python script does the following: Creates the agent Instantiates an AI agent using the selected model (gpt-4o, for example), adds tool access, and sets instructions that tell the agent how to behave (e.g., “You are a helpful SaaS assistant…”). Creates a conversation thread A thread is started to hold a conversation between the user and the agent. Posts a user message Sends a question like “Why did my billing spike for tenant_456 this week?” to the agent. Processes the request The agent reads the message, determines that it should use the custom tool to retrieve usage data, and processes the query. Displays the response Prints the response from the agent with a natural language explanation based on the tool’s output. # billing_anomaly_agent.py import os from datetime import datetime from azure.ai.projects import AIProjectClient from azure.identity import DefaultAzureCredential from azure.ai.projects.models import FunctionTool, ToolSet from dotenv import load_dotenv from pprint import pprint from billing_agent_tools import user_functions # Custom tool function module # Load environment variables from .env file load_dotenv() # Create an Azure AI Project Client project_client = AIProjectClient.from_connection_string( credential=DefaultAzureCredential(), conn_str=os.environ["PROJECT_CONNECTION_STRING"], ) # Initialize toolset with our user-defined functions functions = FunctionTool(user_functions) toolset = ToolSet() toolset.add(functions) # Create the agent agent = project_client.agents.create_agent( model=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"], name=f"billing-anomaly-agent-{datetime.now().strftime('%Y%m%d%H%M')}", description="Billing Anomaly Detection Agent", instructions=f""" You are a helpful SaaS financial assistant that retrieves and explains billing anomalies using usage data. The current date is {datetime.now().strftime("%Y-%m-%d")}. """, toolset=toolset, ) print(f"Created agent, ID: {agent.id}") # Create a communication thread thread = project_client.agents.create_thread() print(f"Created thread, ID: {thread.id}") # Post a message to the agent thread message = project_client.agents.create_message( thread_id=thread.id, role="user", content="Why did my billing spike for tenant_456 this week?", ) print(f"Created message, ID: {message.id}") # Run the agent and process the query run = project_client.agents.create_and_process_run( thread_id=thread.id, agent_id=agent.id ) print(f"Run finished with status: {run.status}") if run.status == "failed": print(f"Run failed: {run.last_error}") # Fetch and display the messages messages = project_client.agents.list_messages(thread_id=thread.id) print("Messages:") pprint(messages["data"][0]["content"][0]["text"]["value"]) # Optional cleanup: # project_client.agents.delete_agent(agent.id) # print("Deleted agent") Run the agent: To run the agent, run the following command python billing_anomaly_agent.py Snippet of output from agent: Using the Azure AI Foundry Agent Playground: After running your agent using the Azure AI Agent SDK, it is saved within your Azure AI Foundry project. You can now experiment with it using the Agent Playground. To try it out: Go to the Agents section in your Azure AI Foundry workspace. Find your billing anomaly agent in the list and click to open it. Use the playground interface to test different financial or billing-related questions, such as: “Did tenant_456 exceed their API usage quota this month?” “Explain recent storage usage changes for tenant_456.” This is a great way to validate your agent's behavior without writing more code. Summary: You’ve now created a working AI agent that talks to your Postgres database, all using: A simple Python function Azure AI Agent Service A Neon Serverless Postgres backend This approach is beginner-friendly, lightweight, and practical for real-world use. Want to go further? You can: Add more tools to the agent Integrate with vector search (e.g., detect anomaly reasons from logs using embeddings) Resources: Introduction to Azure AI Agent Service Develop an AI agent with Azure AI Agent Service Getting Started with Azure AI Agent Service Neon on Azure Build AI Agents with Azure AI Agent Service and Neon Multi-Agent AI Solution with Neon, Langchain, AutoGen and Azure OpenAI Azure AI Foundry GitHub Discussions That's it, folks! But the best part? You can become part of a thriving community of learners and builders by joining the Microsoft Learn Student Ambassadors Community. Connect with like-minded individuals, explore hands-on projects, and stay updated with the latest in cloud and AI. 💬 Join the community on Discord here and explore more benefits on the Microsoft Learn Student Hub.Ensuring High Availability for Web Applications using Azure App Service and Azure Traffic Manager
As a student entrepreneur, the journey of building and launching projects often coincides with academic commitments. Downtime or poor application performance can disrupt our ventures, impact potential opportunities, and even hinder academic progress. Balancing coursework and startup ambitions can be demanding, making the reliability of our web applications crucial. In this blog post, we explore how Azure App Service and Azure Traffic Manager provide accessible solutions to tackle these challenges, ensuring our projects remain available to users 24/7, regardless of academic demands.
