Understanding MCP (Model Context Protocol) with Examples

Introduction

The Model Context Protocol (MCP) is a structured way to manage and exchange contextual information between AI models and applications. It allows AI systems to maintain state, remember previous interactions, and improve response relevance in multi-turn conversations.

Why is MCP Needed?

Most AI models process each request independently. Without context, they cannot recall previous interactions, leading to disjointed conversations. MCP solves this by providing a framework to:

• Maintain conversation history
• Track user preferences
• Improve response accuracy
• Call external tools and APIs to enhance responses

How MCP Works

MCP operates using context objects that store relevant details about an interaction. These context objects can include:

• Session ID: Unique identifier for a conversation
• User Information: Preferences, history, and settings
• Previous Queries & Responses: Helps maintain continuity
• Domain-Specific Knowledge: Relevant facts that improve AI accuracy
• API & Tool Calls: Enables dynamic responses by fetching real-time data

Example of MCP in Action

Let's say we are building an AI assistant for customer support. Without MCP, the conversation might look like this:

Without MCP

User: "What's my order status?"

AI: "Please provide your order ID."

User: "It's #12345."

AI: "Your order is in transit."

Here, the AI forgets the user after every message and needs additional input each time.

With MCP

Using MCP, we store the session details and user data:

{
  "session_id": "abc123",
  "user": {
    "name": "John Doe",
    "email": "[e-mail]"
  },
  "context": {
    "last_order_id": "12345",
    "last_query": "order_status"
  }
}

Now, the conversation is smoother:

User: "What's my order status?"

AI: "Your last order (#12345) is in transit."

Since the AI remembers the order ID from context, it eliminates the need to ask again.

Using Tools and APIs in MCP

MCP also allows AI systems to call external APIs and tools dynamically. For example, if a user asks for real-time weather updates, MCP can fetch data from a weather API:

{
  "session_id": "xyz789",
  "user": {"name": "Alice"},
  "context": {"last_query": "weather"},
  "api_call": {
    "endpoint": "https://weatherapi.com/current",
    "parameters": {"location": "New York"}
  }
}

The AI can then return:

User: "What's the weather like in New York?"

AI: "It's 72°F and sunny in New York."

By integrating API calls, MCP enables AI assistants to provide real-time, accurate responses beyond static knowledge.

Implementing MCP

To implement MCP, you need:

1. Session Management – Store session data using a database or memory cache (e.g., Redis).
2. Context Storage – Maintain a structured context object.
3. Stateful APIs – Modify API calls to include and update context data.
4. Tool and API Integration – Enable AI to fetch external data dynamically.

Example: Context-Aware API in Python

from flask import Flask, request, jsonify

app = Flask(__name__)
context_store = {}

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

    if session_id not in context_store:
        context_store[session_id] = {"history": []}

    context_store[session_id]["history"].append(user_message)

    response = generate_response(user_message, context_store[session_id])
    return jsonify({"response": response})

def generate_response(message, context):
    return f"You said: {message}. Context length: {len(context['history'])}"

if __name__ == '__main__':
    app.run(debug=True)

Conclusion

MCP helps AI models maintain state and improve conversational flow. By structuring context information efficiently and integrating tools and APIs, AI assistants can provide more meaningful and personalized responses, leading to better user experiences.

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What are your thoughts on MCP? Have you implemented something similar? Let me know in the comments!