Unlocking GenAI Potential: How MCP Servers Revolutionize Model Context Protocols

Aryan JunejaAryan Juneja
6 min read

Table of contents

🚀 Unlocking MCP Servers 🧠: The Backbone of Context-Aware GenAI Applications

📋 Table of Contents

📘 Introduction

Generative AI (GenAI) is transforming how we build intelligent applications, from chatbots to code assistants. But have you ever wondered how these systems maintain context across complex, multi-turn conversations? Or how they seamlessly integrate external knowledge and user data to provide relevant, personalized responses?

Enter the Model Context Protocol (MCP) and MCP servers—a rising standard for managing and sharing context between AI models and applications. If you’re building with LLMs or multimodal models, understanding MCP is quickly becoming essential.

In this article, you’ll learn:

  • What MCP and MCP servers are, and why they matter for GenAI
  • How to set up and interact with an MCP server
  • How to build a context-aware chatbot using MCP
  • Practical code examples you can adapt for your own projects

By the end, you’ll be ready to supercharge your GenAI apps with robust, scalable context management. Ready to level up? Let’s dive in!

🧠 What is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open protocol designed to standardize how AI models and applications exchange, manage, and update context. Think of it as the “language” that lets your GenAI models remember, retrieve, and update information across sessions and users.

Key Capabilities of MCP:

  • Context Storage & Retrieval: Store and fetch user, session, or application context efficiently.
  • Model-Agnostic: Works with any LLM or multimodal model, not tied to a specific vendor.
  • Interoperability: Enables seamless integration between different AI tools and services.
  • Scalability: Designed for high-throughput, multi-user environments.

One-liner:
MCP is the glue that lets GenAI apps remember, reason, and personalize at scale.

✅ Prerequisites

Before you start, make sure you have:

  • Python 3.8+ (for code examples)
  • Basic knowledge of REST APIs and Python HTTP libraries (e.g., requests)
  • Familiarity with GenAI concepts (LLMs, context windows, etc.)
  • An MCP server (we’ll use OpenMCP for demonstration)
  • API key or local deployment (for OpenMCP, you can run locally)

Installation Commands

# Install Python dependencies
pip install requests flask

# (Optional) Clone and run OpenMCP server locally
git clone https://github.com/modelcontext/openmcp.git
cd openmcp
pip install -r requirements.txt
python server.py  # Starts MCP server on localhost:8000 by default

🚀 Use Case: Building a Context-Aware Chatbot with MCP Server

Let’s build a context-aware chatbot that remembers user preferences and conversation history across sessions.

Problem Statement

Traditional chatbots often “forget” previous interactions, leading to repetitive or irrelevant responses. We want a chatbot that:

  • Remembers user preferences (e.g., favorite topics)
  • Maintains conversation history
  • Personalizes responses based on stored context

Visual Workflow

📥 User Message
→ 🤔 Chatbot (LLM) queries MCP server for context
→ 🧠 MCP server returns relevant context
→ 💬 Chatbot generates response using context
→ 📤 Response sent to user & context updated in MCP

Benefits & Applications

  • Personalized user experiences
  • Seamless multi-turn conversations
  • Easy integration with multiple AI models

Real-World Context

This pattern is used in customer support bots, AI tutors, and virtual assistants—anywhere context continuity is key.

🧩 Code Examples

Let’s break down the core interactions with an MCP server.

1. Storing Context

import requests

MCP_URL = "http://localhost:8000/context"
user_id = "user_123"
context_data = {
    "favorite_topic": "Python",
    "conversation_history": ["Hi!", "Tell me about decorators."]
}

# Store context for a user
response = requests.post(
    f"{MCP_URL}/{user_id}",
    json=context_data
)
print("Store context:", response.json())

2. Retrieving Context

# Retrieve context for a user
response = requests.get(f"{MCP_URL}/{user_id}")
context = response.json()
print("Retrieved context:", context)

3. Updating Context

# Update context (e.g., add new message to history)
context["conversation_history"].append("What about generators?")
response = requests.put(
    f"{MCP_URL}/{user_id}",
    json=context
)
print("Updated context:", response.json())

🧩 Practical Implementation

Let’s build a minimal context-aware chatbot using Flask and OpenMCP.

1. MCP Server Setup

If you haven’t already, start the OpenMCP server:

git clone https://github.com/modelcontext/openmcp.git
cd openmcp
pip install -r requirements.txt
python server.py

By default, it runs at http://localhost:8000.

2. Chatbot Backend (Flask App)

from flask import Flask, request, jsonify
import requests

app = Flask(__name__)
MCP_URL = "http://localhost:8000/context"

def get_context(user_id):
    resp = requests.get(f"{MCP_URL}/{user_id}")
    return resp.json() if resp.status_code == 200 else {}

def update_context(user_id, context):
    requests.put(f"{MCP_URL}/{user_id}", json=context)

def generate_response(user_message, context):
    # Simulate LLM response using context
    history = context.get("conversation_history", [])
    favorite = context.get("favorite_topic", "AI")
    reply = f"You said: '{user_message}'. Last topic: {favorite}. Previous: {history[-1] if history else 'None'}"
    return reply

@app.route("/chat", methods=["POST"])
def chat():
    data = request.json
    user_id = data["user_id"]
    user_message = data["message"]

    # 1. Retrieve context
    context = get_context(user_id)
    history = context.get("conversation_history", [])
    history.append(user_message)
    context["conversation_history"] = history

    # 2. Generate response
    response = generate_response(user_message, context)

    # 3. Update context
    update_context(user_id, context)

    return jsonify({"response": response})

if __name__ == "__main__":
    app.run(port=5000)

3. Testing the Chatbot

import requests

user_id = "user_123"
messages = [
    "Hi!",
    "Tell me about Python decorators.",
    "What about generators?"
]

for msg in messages:
    resp = requests.post(
        "http://localhost:5000/chat",
        json={"user_id": user_id, "message": msg}
    )
    print("Bot:", resp.json()["response"])

✅ Output Example

Here’s what a sample interaction looks like:

Bot: You said: 'Hi!'. Last topic: AI. Previous: None
Bot: You said: 'Tell me about Python decorators.'. Last topic: AI. Previous: Hi!
Bot: You said: 'What about generators?'. Last topic: AI. Previous: Tell me about Python decorators.

Notice how the bot references previous messages—thanks to context stored in the MCP server!

📦 Next Steps/Resources

🧠 Final Thoughts

In this article, you explored how MCP servers provide a robust, scalable way to manage context for GenAI applications. You learned how to:

  • Set up and interact with an MCP server
  • Build a context-aware chatbot that remembers users
  • Use practical code examples to store, retrieve, and update context

Key takeaway:
MCP is the missing link for building truly intelligent, context-aware AI systems.

As GenAI continues to evolve, mastering context management will set your applications apart. So why not experiment with MCP in your next project? The future of smarter, more personalized AI is just a protocol away! 🚀

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Context ManagementAI ServersAI infrastructuregenaillmMachine LearningmcpModel Context ProtocolPrompt Engineeringscalability

Written by

Aryan Juneja
Aryan Juneja