Reimagining AI Integration: The Power of MCP
Imagine a universal connector for AI applications—just like USB-C simplifies hardware connections, the Model Context Protocol (MCP) is revolutionizing how large language models (LLMs) interact with tools, data, and applications. MCP is an open protocol that simplifies the process of delivering context to LLMs, empowering developers to build powerful, intelligent agent-based solutions.
The concept of MCP originated from the challenges developers faced when building context-aware AI agents on top of large language models (LLMs) like GPT, Claude, or Gemini. These LLMs are stateless by design, meaning they don’t retain memory between interactions unless you provide that memory explicitly.
To solve this, Microsoft and the Azure SDK team introduced the Model Context Protocol, a vendor-agnostic open standard designed to manage and structure the context AI models receive from external tools and data sources.
Why Choose MCP?
MCP is purpose-built for developing intelligent agents and orchestrating complex workflows on top of LLMs. These AI models often need to interface with external data and services, and MCP provides a standardized way to make that integration seamless. Key benefits include:
-
🔌 Plug-and-play integrations: An expanding library of pre-built connections that LLMs can access out of the box.
-
Cross-platform compatibility: Avoids being locked into a single AI provider by offering flexible backend switching.
-
Secure by design: Encourages implementation of best practices for data protection within enterprise environments.
⚙️ Core Components of MCP
MCP changes how models manage and retrieve context, boosting their accuracy and conversational coherence. It introduces a structured framework with the following core elements:
-
Context Repository – Centralized storage for past interactions, queries, and AI outputs.
-
Dynamic Context Injection – Inserts relevant context during runtime to improve AI understanding.
-
Protocol Standardization – Ensures all contextual data is processed uniformly and accurately.
-
Adaptive Query Processing – Learns from previous interactions to tailor responses more precisely.
At its core, MCP follows a client-server architecture:
MCP Hosts: Applications like Claude Desktop or IDEs that want to access data through MCP.
MCP Clients: Protocol clients maintaining 1:1 connections with servers.
MCP Servers: Lightweight programs exposing specific capabilities through the standardized
MCP vs. Traditional RAG
While Retrieval-Augmented Generation (RAG) helps LLMs pull in external information, MCP elevates the approach by wrapping it in a smart context-management layer. Here's how it expands upon RAG:
-
Structures retrieved data into meaningful, persistent context
-
Introduces consistent communication protocols across sessions
-
Minimizes hallucinations with better historical awareness
-
Enables more nuanced and accurate responses through contextual refinement
In short, MCP transforms RAG into a system that not only retrieves information but sustains relevance across ongoing conversations.
MCP in Action: Azure MCP Server
Microsoft’s Azure MCP Server, now in public preview, brings MCP to life. It acts as a smart interface between AI agents and Azure’s cloud services, making it easier to:
-
Query data in Azure Cosmos DB
-
Read/write files in Azure Storage
-
Analyze system logs using Azure Monitor (KQL)
-
Manage settings through Azure App Configuration
-
Execute commands using Azure CLI
With just one command, developers can spin up the Azure MCP Server:
This sets up a robust backend that’s ready to handle interactions from any MCP-compliant AI agent.
MCP in Action: Azure AI Foundry
Enhanced Capabilities with MCP.
MCP in Action: Azure AI Foundry
Microsoft's Azure AI Foundry showcases how MCP isn't just theory—it’s a practical, production-ready approach to enhancing AI experiences across enterprise applications. Azure AI Foundry leverages the Model Context Protocol to bring together large language models, business data, and cloud services into a cohesive, intelligent system.
By embedding MCP into Azure AI Foundry, Microsoft enables organizations to:
-
Supercharge AI Search
Enterprise users can conduct intelligent searches across internal knowledge bases, file systems, and documentation. Thanks to MCP, these searches remain context-aware—tracking the history of what was asked before and refining answers accordingly. -
Build Smarter Virtual Assistants
MCP makes it possible for virtual agents to maintain ongoing memory of a conversation. Whether it's a support bot, a sales assistant, or an internal service desk, Azure AI Foundry uses MCP to keep interactions fluid, relevant, and consistent across sessions. -
Improve Decision-Making
By integrating with internal databases and telemetry sources, Foundry-enabled agents can analyze real-time operational data and provide actionable insights—helping decision-makers move from raw data to smart recommendations faster. -
Seamless Knowledge Base Integration
With MCP’s structured approach, AI agents can easily connect to CRMs, wikis, ticketing systems, and document repositories—dynamically injecting relevant content during user interactions. -
Enhance RAG Workflows
Azure AI Foundry builds on traditional Retrieval-Augmented Generation (RAG) models, using MCP to structure and persist retrieved data, ensuring conversations maintain continuity and context over time.
Collaborating with Semantic Kernel
For developers, MCP aligns perfectly with Semantic Kernel, Microsoft's open-source framework for integrating AI models with various data sources., developers can unlock the full potential of MCP for their projects.
Working with Semantic Kernel
MCP is fully compatible with Semantic Kernel, Microsoft’s open-source SDK designed to integrate AI models with real-world data sources. This synergy enables developers to build more intelligent, context-aware applications quickly.
Developers can use Semantic Kernel to extend MCP’s capabilities even further—whether for enterprise chatbots, intelligent assistants, or workflow automation solutions.
Example: Integrating MCP Server with Semantic Kernel
Scenario: Query Processing Workflow
A developer wants to create an AI agent that interacts with multiple data sources, such as a local database, APIs, and file systems, using Semantic Kernel and the MCP Server.
Steps:
MCP Configuration Begin by setting up the MCP Server to act as an intermediary for context handling. The server manages connections to various data sources.
{
"MCPServer": {
"DataSources": [
{ "Type": "LocalDatabase", "Connection": "db_connection_string" },
{ "Type": "API", "Endpoint": "https://api.example.com" },
{ "Type": "FileSystem", "Path": "/user/files" }
]
}
}
Semantic Kernel Code
Using Semantic Kernel, you create a skill that interacts with the MCP Server:
from semantic_kernel import SemanticKernel
from mcp_client import MCPClient
# Initialize MCP Client
mcp_client = MCPClient(server_url="http://localhost:8000")
# Initialize Semantic Kernel
kernel = SemanticKernel()
# Define Skill for MCP Query
def query_mcp_skill(context):
query = context["query"]
response = mcp_client.query(query)
return response
# Register Skill
kernel.register_skill("QueryMCP", query_mcp_skill)
# Execute Skill
input_context = {"query": "Retrieve latest sales data"}
result = kernel.execute_skill("QueryMCP", input_context)
print(result) # Outputs data retrieved via MCP Server
Dynamic Context Injection Semantic Kernel can dynamically inject context into the query based on user interaction history:
def dynamic_context_skill(context):
user_history = context["history"]
context["query"] = f"{context['query']} based on {user_history}"
return context["query"]
kernel.register_skill("DynamicContext", dynamic_context_skill)
Using Semantic Kernel with MCP Tools Microsoft provides detailed guides for using Semantic Kernel with MCP tools to streamline workflows. This allows developers to:
Fetch relevant context from MCP Server.
Enable dynamic skill chaining for complex workflows.
Maintain context-awareness across interactions.
