Unlocking Scalable AI Integrations with MCP: Insights and Implementation (Part 3)

Power Your AI with Dynamic Knowledge: MCP x Azure AI Agent Service

Author

Sumeth Manasikarn
April 20, 2025

Reading Time: 10 min ⏱️

Introduction

In Part 1 of this series, we introduced the Model Context Protocol (MCP), an open standard enabling seamless integration between AI models and external data sources. We demonstrated how to build a Perplexity-powered MCP server to visualize industry adoption trends.

In Part 2, we took a deeper dive into Retrieval-Augmented Generation (RAG) with Vectorize. We showed how to set up an MCP server to automate document parsing and vector search for real-world invoice analysis.

In this third installment, we explore how to integrate MCP with Azure AI Agent Services through Azure AI Foundry. This approach offers a scalable path for orchestrating advanced AI workflows using Microsoft’s ecosystem.

To provide proper context, it is essential to understand the capabilities that Azure AI Agent Service brings to the integration landscape.

Azure AI Agent Service is a fully managed platform from Microsoft designed to help developers build, deploy, and scale AI agents that can automate a wide range of tasks and workflows. These agents function as "smart" microservices, combining generative AI models with tools to interact with real-world data sources and perform actions.

The Benefits of Azure AI Agent Services

Figure 1: Azure AI Agent Service

Azure AI Agent Services empower developers to build agentic applications with:

  • Modular and reusable agents that can reason, plan, and act across multiple tools and data sources.

  • Built-in orchestration for multi-step tasks across plugins, APIs, or internal logic.

  • Dynamic tool calling via Azure OpenAI Service and Function Calling.

  • Support for grounding agents in enterprise data through Azure AI Search, Azure Cosmos DB, and Microsoft Graph.

  • Seamless integration with Azure ML, DevOps, and CI/CD pipelines.

These capabilities provide the perfect complement to MCP’s interoperability design, enabling developers to unlock dynamic context across organizational systems.

Azure AI Foundry and Azure AI Agent Services Explained

Azure AI Foundry provides the foundation for building, deploying, and managing AI agents. It includes:

Figure 2: Azure AI Foundry Studio

  • Agent creation UI: Design and publish agents using prompt flow and tool configuration.

  • Observability & debugging tools: Monitor agent runs, usage, and errors.

  • Toolchain integration: Link agents to APIs, web tools, databases, or files.

Azure AI Agent Services sits on top of Foundry to:

  • Run agents on-demand or in response to events.

  • Connect to Azure OpenAI models (like GPT-4o-mini) with Knowledge or Function Calling.

  • Manage agent lifecycles, versions, and tool usage policies.

Together, these services offer a robust infrastructure for intelligent agent workflows.

Use Case: Analyzing TripAdvisor Reviews to Create Marketing Campaign with MCP and Azure AI Agents

Figure 3: Example of three marketing campaigns to promote Koh Samui luxury stays to international travelers from the aggregated sentiment and topic data on Centara Reserve Samui

Figure 4: Recent user reviews for Centara Reserve Samui, including the date, rating, and review text

To demonstrate this integration in action, let's explore a real-world implementation scenario.

Business Background:

A travel agency is a business or organization that provides travel and tourism-related services to individuals or groups, acting as an intermediary between travelers and travel service providers such as airlines, hotels, car rental companies, and tour operators. The main purpose of a travel agency is to help clients plan, organize, and book all aspects of their trips, making travel easier, more convenient, and often more cost-effective.

Key Functions of a Travel Agency

  • Booking flights, hotels, rental cars, cruises, and other transportation or accommodation services.

  • Arranging package tours that may include sightseeing, activities, and entertainment.

  • Providing expert advice on travel destinations, visa requirements, travel insurance, and health or safety guidelines.

  • Creating customized itineraries tailored to the client's preferences, budget, and needs.

  • Assisting with travel documentation, such as passports, visas, and insurance.

  • Offering customer support before, during, and after the trip, including handling changes, cancellations, or emergencies.

Types of Travel Agencies

No.

Type

Description

1

Retail Travel Agency

Sells travel products and services directly to the public on behalf of suppliers.

2

Wholesale Travel Agency

Buys travel components in bulk from vendors and resells them, often as package tours.

3

Online Travel Agency (OTA)

Operates primarily through digital platforms, offering online booking and comparison tools.

4

Specialized/Niche Agencies

Focus on specific travel types, such as business, adventure, luxury, or group travel.

Problem:

A travel agency wants to analyze user reviews from TripAdvisor to identify trending topics, detect sentiment changes, and generate insights for marketing campaign.

Solution:

We create an MCP server that:

  • Accepts search queries related to travel destinations.

  • Calls an Azure AI Agent that pulls TripAdvisor data, processes it using GPT-4o-mini, and returns a structured summary through a series of multi-turn prompts

  • Here's a breakdown of the 6-step prompt chain workflow:

Step

Action

Description

Example Prompt Sequence

1

fetch_hotels

Search hotels and retrieve location_id, name, etc.

Search and list the TripAdvisor location IDs and names for the top 5 luxury hotels in Koh Samui.

2

fetch_reviews

Pull reviews in paginated batches per hotel

Retrieve the 10 most recent user reviews for hotel with location ID 302446 (Melia Koh Samui). Include the date, rating, and review text.

3

summarize_batch

Analyze each batch for topics and sentiment

Summarize the key topics and sentiment trends from the following 10 reviews. Output positive themes, negative themes, and emotional tone.

4

merge_insights

Combine summaries across all batches

Combine insights from all batches of reviews for each hotel. What are the top recurring themes across positive and negative reviews? Which ones are unique to this hotel?

5

compare_hotels

Cross-analyze hotels by themes, tone, and differentiators

Compare the summarized review insights of the 5 hotels. What differentiators can you find in guest experiences, and what are shared commonalities?

6

marketing_insights

Generate marketing strategies from synthesized insights

From the aggregated sentiment and topic data across these hotels, suggest 3 marketing campaigns to promote Koh Samui luxury stays to international travelers.

Technical Integration Architecture:

  1. Claude Desktop App acts as the MCP Host.

  2. MCP Client sends a search query via JSON-RPC.

  3. Custom MCP Server invokes the Azure AI Agent.

  4. Azure AI Agent retrieves and analyzes the data.

  5. Claude displays the results through an interactive dashboard and text summary

This architecture blends local control (MCP) with cloud intelligence (Azure Agents).

Step-by-Step Guide: Creating an MCP Server with Azure AI Agent Service for TripAdvisor Marketing Campaign

Figure 5: Three Targeted Marketing Campaigns for Koh Samui Luxury Stays

Use Case Ideas Across Business Functions

Here are some creative ways to combine MCP + Azure AI Agent Services:

  • Healthcare: Query patient data from FHIR DBs using Azure Agent tools for diagnosis suggestions.

  • Finance: Fetch transaction records and run anomaly detection using Azure Agent ML pipelines.

  • Retail: Connect to product databases to generate AI-curated bundles and price insights.

  • Customer Support: MCP retrieves customer queries; Azure Agents synthesize responses from ticket history.

  • Compliance: MCP sends documents to an Azure Agent for clause analysis and risk detection.

Conclusion

The integration of MCP with Azure AI Agent Services creates a powerful new frontier for scalable, intelligent, and dynamic AI workflows. This combination delivers both the structured flexibility of MCP and the robust orchestration capabilities of Azure Agents.

This powerful fusion enables developers and teams to build more autonomous, context-aware systems—from enhancing LLM contexts to deploying AI-driven insights in production environments.

If you've found value in this MCP implementation guide, share it with your development team and technical architects. Together, we can leverage MCP standards to build more robust, interconnected AI systems that drive organizational innovation.

In Part 4 of this series, we'll dive into advanced integration patterns, provide detailed implementation examples, and show you how to create production-ready AI workflows that harness MCP and external data sources.

Don't forget to subscribe to AI Productivity Insights for free to get the latest developments in AI integration patterns, implementation guides, and industry best practices. Our weekly newsletter brings carefully curated insights straight to your inbox, helping you work smarter, not harder, by keeping you at the cutting edge of AI innovation.