Demystifying MCP Servers: The Backbone of Smarter AI Agent Integration – Blog

As artificial intelligence continues to evolve, especially in the realm of chat-based agents like Microsoft Copilot, OpenAI GPT agents, and custom virtual assistants, a new term has entered the spotlight: MCP server. You may have seen the acronym floating around in developer documentation, GitHub repositories, or product demos.

But what exactly is an MCP server? Why is it relevant now? And how can it simplify and supercharge how your AI agents interact with business systems, APIs, and data sources?

Let’s take a deeper dive into what Model Context Protocol (MCP) servers are, how they work, and how you can use them to enhance your own AI experiences—complete with examples, architecture diagrams, and developer guidance.

What Is a Model Context Protocol (MCP) Server?

Imagine you’re building an AI assistant that helps employees perform tasks like checking their PTO balance, submitting IT tickets, or finding HR policies. All of those actions require the assistant to communicate with various backend systems—databases, SaaS tools, REST APIs, and more.

Traditionally, for every data source or service your AI assistant needs to connect to, you would have to:

Understand and implement that system’s unique API
Authenticate using OAuth, API keys, or custom security models
Parse and transform responses specific to each service
Handle edge cases like timeouts, errors, or pagination
Write and maintain custom integration logic for each connection

This can become overwhelming very quickly, especially when the number of connected systems increases.

Model Context Protocol (MCP) was created to solve this problem.

An MCP server acts as a middle layer between your AI agent and all of the backend systems it needs to interact with. It abstracts away the complexity of individual integrations by presenting a standardized, unified way for your AI to query, update, and route data between systems.

You can think of it as a translator, router, and orchestrator all in one.

Why MCP Matters for AI Agents Like Copilot

Before MCP, creating a conversational AI assistant meant writing a unique connector for each platform or API. For example, if your AI needed to:

Query data from SharePoint
Submit a ticket to ServiceNow
Check email availability via Microsoft Graph
Get CRM data from Salesforce

You would need to build and manage four different connections, each with its own quirks, response formats, and authentication methods.

With MCP, your AI agent talks to a single endpoint (the MCP server), and that server takes care of everything else. It can call APIs, perform lookups, make decisions based on the data it receives, and even route follow-up tasks dynamically.

This is especially useful in situations where the conversation with the user spans multiple systems, such as:

Example scenario:
“Can you show me our onboarding steps for new hires and also update the checklist to include remote employees?”

To fulfill that request, your AI might need to:

Query a SharePoint document or internal KB.
Ask clarifying questions.
Submit a change ticket in ServiceNow.
Follow up based on user input.

A static, pre-configured flow (like those built in Power Automate) would struggle here. It lacks memory, adaptability, and flexibility. But an MCP server can handle this easily, especially if it supports context and state.

Stateless vs Stateful MCP Servers

There are two broad categories of MCP servers, and which one you use depends on the complexity of your use case.

Stateless MCP Server

A stateless MCP server is the simplest type. It does not store any memory or session information between requests. It simply receives a request, performs the necessary task, and returns a result.

Use cases:

Basic data lookups
API passthroughs
Read-only tasks

Advantages:

Easy to build and deploy
Lightweight
No memory management needed

Architecture:

Copilot or AI agent → MCP Server → External API → Response

Stateful MCP Server

A stateful MCP server, sometimes referred to as a “smart MCP,” takes things further. It can remember prior user interactions, track multi-step processes, and make intelligent decisions using AI models.

Use cases:

Multi-step workflows
Context-aware conversations
Intelligent routing and orchestration

Advantages:

Greater conversational capability
Dynamic behavior based on context
Can integrate LLMs (like GPT) to guide decisions

Architecture:

Copilot or AI agent → Smart MCP Server
Smart MCP Server → [KB System]
Smart MCP Server → [ITSM Tool]
Smart MCP Server → [CRM / Custom DB]
Smart MCP Server → Memory / Storage

Prebuilt MCP Servers: Use Before You Build

You don’t always need to build an MCP server from scratch. In fact, many popular platforms already have open-source MCP servers built and maintained by the community. These are ready to plug into your project with minimal configuration.

Examples include:

Microsoft 365 (Outlook, Teams, SharePoint)
Google Workspace
Salesforce
ServiceNow
Notion
GitHub
Azure DevOps

Where to find them:
Visit the Awesome MCP Servers GitHub list for a continuously updated index of connectors built by the community.

This is the best starting point if you want to test what MCP can do without writing your own server.

Building Your Own MCP Server

If you want a fully custom integration, or need to connect to private/internal systems that don’t already have a prebuilt MCP, building your own server is simple and flexible.

Step 1: Decide on Stateless or Stateful

Start by asking yourself:

Does the server need to remember user interactions across steps?
Will the server need to call different systems based on previous outputs?

If yes, go with a stateful server. If no, stateless will be sufficient.

Step 2: Choose Your Technology Stack

Most MCP servers are written in either:

Python (using frameworks like FastAPI or Flask)
Node.js (using Express or similar libraries)

They can be hosted on:

Azure App Services
Azure Container Apps
AWS Lambda or API Gateway
Google Cloud Run

Make sure to include proper authentication, logging, and monitoring.

Use tools like:

Azure Key Vault (for secure API key storage)
Azure Application Insights or Monitor (for logging and diagnostics)
Azure Front Door or API Management (for securing access)

Sample: Simple Stateless MCP Server in Python

Here’s a minimal example using FastAPI:

from fastapi import FastAPI, Request
import requests

app = FastAPI()

@app.post("/invoke")
async def invoke(request: Request):
    data = await request.json()
    query = data.get("query")

    # Example call to a knowledge base API
    response = requests.get(f"https://api.internal.acme.com/search?q={query}")
    return {"result": response.json()}

Sample request from your AI agent:

{
  "query": "Show me the remote work onboarding checklist"
}

Sample response:

jsonCopyEdit{
  "result": {
    "title": "Remote Employee Onboarding v2.3",
    "lastUpdated": "2025-06-01"
  }
}

Making It Smart: Adding State and AI

To add intelligence and memory to your MCP server:

Store conversation history in a backend database (e.g. Redis, Cosmos DB, PostgreSQL)
Use that history when handling each new request
Add OpenAI (or Azure OpenAI) to help determine what to do next

Example pseudocode:

history = get_conversation_history(user_id)
current_query = get_latest_input()
combined_context = history + current_query

intent = call_openai_to_classify_intent(combined_context)

if intent == "create_ticket":
    create_servicenow_ticket()
elif intent == "query_kb":
    search_knowledge_base()

Feature Comparison

Feature	Stateless MCP Server	Stateful MCP Server
Supports API connectivity	Yes	Yes
Maintains conversation memory	No	Yes
Can perform multi-step flows	No	Yes
Suitable for simple tasks	Yes	Yes
Suitable for complex agents	Limited	Yes
Easy to deploy and manage	Yes	Medium

Conclusion

MCP servers are quickly becoming a foundational piece of modern AI architecture. As conversational agents like Microsoft Copilot, OpenAI GPTs, and custom virtual assistants become more powerful, they also need to talk to the systems where your data lives.

Whether you want to connect to an HR system, internal database, CRM, or ticketing platform—an MCP server gives your AI agent a standardized way to interact with everything.

Use a prebuilt connector where possible, or build your own for ultimate flexibility. Either way, you’ll be enabling your AI to do more, faster, and more intelligently.