Unlocking the Power of MCP Servers: Model Context Providers in Generative AI

Aryan JunejaAryan Juneja
6 min read

Table of contents

🚀 Unlocking the Power of MCP Servers in GenAI 🧠: How Model Context Providers Supercharge Generative AI Applications

📋 Table of Contents

  1. Introduction
  2. What is an MCP Server?
  3. Prerequisites
  4. Use Case: Dynamic Context Injection for GenAI Chatbots
  5. Code Examples
  6. Practical Implementation
  7. Output Example
  8. Next Steps/Resources
  9. Final Thoughts

📘 Introduction

Generative AI (GenAI) is revolutionizing the way we build intelligent applications, from chatbots to content generators. But have you ever wondered how these models can stay relevant, personalized, and context-aware in real time? Enter MCP servers—Model Context Providers—a game-changing architectural pattern that lets you inject dynamic, up-to-date context into your GenAI workflows.

In this article, you'll learn:

  • What MCP servers are and why they're crucial for GenAI
  • How to set up and use an MCP server to provide real-time context to your models
  • Step-by-step code examples for building and integrating an MCP server
  • How to supercharge your GenAI chatbot with dynamic, user-specific context

By the end, you'll be ready to build smarter, more responsive GenAI applications that truly understand and adapt to your users. Ready to level up your AI game? Let's dive in!

🧠 What is an MCP Server?

An MCP server (Model Context Provider) is a service that supplies external, dynamic context to generative AI models at inference time. Think of it as a real-time "context engine" that feeds your model with the latest user data, preferences, or external knowledge—right when it needs it.

Key capabilities of MCP servers:

  • Dynamic Context Injection: Provide up-to-date information (user profiles, recent activity, external APIs) to the model on demand.
  • Separation of Concerns: Decouple context management from model logic, making your GenAI stack modular and maintainable.
  • Scalability: Serve context to multiple models or applications simultaneously.
  • Security & Privacy: Centralize access control and data governance for sensitive context.

One-liner:
MCP servers are the "context butlers" of GenAI, delivering exactly the right information to your models at the perfect moment.

✅ Prerequisites

To follow along with the code and implementation, you'll need:

  • Python 3.8+
  • FastAPI (for the MCP server)
  • OpenAI API key (or similar GenAI provider)
  • Basic knowledge of REST APIs and GenAI prompt engineering
  • Installation commands:
pip install fastapi uvicorn openai
  • (Optional) ngrok for exposing your local MCP server to the internet (for cloud-based GenAI models)

🚀 Use Case: Dynamic Context Injection for GenAI Chatbots

Let's build a GenAI-powered chatbot that can answer user questions with personalized, real-time context—like their recent orders or preferences.

Problem Statement:
How can we make a chatbot that doesn't just answer generically, but tailors its responses based on up-to-date user data?

Workflow:
📥 User Message → 🤔 Chatbot Calls MCP Server for Context → 🧠 GenAI Model Generates Response with Context → 📤 Personalized Reply

Benefits:

  • Hyper-personalized user experiences
  • Real-time adaptation to user behavior
  • Decoupled, maintainable architecture

Real-world context:
Think of customer support bots, virtual assistants, or recommendation engines that need to "know" the user to be truly helpful.

🧩 Code Examples

Let's break down the implementation into two main parts:

  1. MCP Server: A FastAPI service that provides user context.
  2. GenAI Chatbot: A Python script that queries the MCP server and uses the context in prompts.

1. MCP Server (FastAPI)

# mcp_server.py
from fastapi import FastAPI, HTTPException
from typing import Dict

app = FastAPI()

# Simulated user context database
USER_CONTEXT = {
    "alice": {"recent_order": "Noise-cancelling headphones", "loyalty_level": "Gold"},
    "bob": {"recent_order": "Smartwatch", "loyalty_level": "Silver"},
}

@app.get("/context/{username}")
async def get_user_context(username: str) -> Dict:
    context = USER_CONTEXT.get(username)
    if not context:
        raise HTTPException(status_code=404, detail="User not found")
    return context

Explanation:

  • This FastAPI app exposes a /context/{username} endpoint.
  • It returns user-specific context (e.g., recent order, loyalty level).
  • In a real app, you'd fetch this from a database or external API.

2. GenAI Chatbot (Python + OpenAI)

# chatbot_with_mcp.py
import requests
import openai

openai.api_key = "YOUR_OPENAI_API_KEY"

def get_user_context(username: str, mcp_url: str) -> dict:
    response = requests.get(f"{mcp_url}/context/{username}")
    response.raise_for_status()
    return response.json()

def generate_response(user_message: str, user_context: dict) -> str:
    prompt = (
        f"User is a {user_context['loyalty_level']} member. "
        f"Their recent order: {user_context['recent_order']}.\n"
        f"User says: {user_message}\n"
        "Respond as a helpful assistant."
    )
    completion = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=100,
        temperature=0.7
    )
    return completion.choices[0].text.strip()

if __name__ == "__main__":
    username = "alice"
    user_message = "Can I get a discount on my next purchase?"
    mcp_url = "http://localhost:8000"

    # Step 1: Get context from MCP server
    context = get_user_context(username, mcp_url)

    # Step 2: Generate response using GenAI model
    reply = generate_response(user_message, context)
    print(f"Chatbot: {reply}")

Explanation:

  • Fetches user context from the MCP server.
  • Crafts a prompt that includes the context.
  • Sends the prompt to OpenAI's API and prints the response.

🧩 Practical Implementation

Let's walk through the steps to get this running on your machine.

Step 1: Start the MCP Server

uvicorn mcp_server:app --reload
  • The server will run at http://localhost:8000.

Step 2: Test the MCP Endpoint

Try fetching context for "alice":

curl http://localhost:8000/context/alice

Expected output:

{"recent_order": "Noise-cancelling headphones", "loyalty_level": "Gold"}

Step 3: Run the Chatbot Script

Update your OpenAI API key in chatbot_with_mcp.py, then run:

python chatbot_with_mcp.py
  • The script will fetch Alice's context and generate a personalized response.

Step 4: Try with Different Users

Change username = "bob" in the script and rerun to see different context in action.

✅ Output Example

Here's what you might see when running the chatbot for Alice:

Chatbot: As a Gold member, you're eligible for exclusive discounts! Since your last order was noise-cancelling headphones, I can offer you a 15% discount on your next purchase. Would you like to hear about our current promotions?

And for Bob:

Chatbot: As a Silver member, you have access to special offers. Since you recently purchased a smartwatch, I can check if there are any related accessories on discount for you. Would you like more details?

📦 Next Steps/Resources

  • FastAPI Docs: https://fastapi.tiangolo.com/
  • OpenAI API Reference: https://platform.openai.com/docs/api-reference
  • ngrok: https://ngrok.com/ (for exposing local servers)
  • Suggested Improvements:
    • Connect MCP to a real database or external APIs
    • Add authentication to the MCP server
    • Support batch context retrieval for multi-user scenarios
    • Integrate with other GenAI providers (Anthropic, Cohere, etc.)
  • Related Topics:
    • Prompt engineering best practices
    • Context window management in LLMs
    • Retrieval-Augmented Generation (RAG) architectures

🧠 Final Thoughts

In this article, you learned how MCP servers (Model Context Providers) can transform your GenAI applications by injecting real-time, personalized context into every model interaction. We built a working example of a context-aware chatbot, demonstrating how easy it is to decouple context management from your model logic.

Key takeaways:

  • MCP servers make your GenAI apps smarter, more relevant, and easier to maintain.
  • Dynamic context injection is essential for personalization and real-world utility.
  • This pattern is applicable to chatbots, recommendation engines, virtual assistants, and beyond.

As GenAI continues to evolve, mastering context management will set your applications apart. So why not experiment with your own MCP server today? The future of truly intelligent AI is context-driven—make sure you're ready for it! 🚀

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Aryan Juneja
Aryan Juneja