Hashtrust AI - Generative AI Solutions

Why is MCP Important?

LLMs alone are powerful but limited by their static nature—they rely on pre-trained data and lack real-time awareness. Many AI applications require external data access to function optimally, such as:

A customer support bot accessing user purchase history.
A financial assistant retrieving livestock prices.
A travel planner fetching real-time weather and flight details.

MCP standardizes how AI models connect to external tools and data sources, providing:

Pre-Built Integrations – LLMs can directly plug into a growing list of APIs and tools.
Vendor Flexibility – Easy switching between different LLM providers (e.g., OpenAI, Anthropic, Mistral).
Security & Compliance – Ensures data security while integrating AI models into enterprise infrastructure.

How MCP Works?

MCP enables AI models to interact with external data sources via a structured middleware layer. This consists of three core components:

1. MCP Adapters

These connect AI models to external APIs, databases, or tools. Examples include:

Weather API Adapter – Fetches real-time weather.
Calendar API Adapter – Checks user availability.
Stock Market Adapter – Retrieves real-time stock prices.

2. MCP Context Providers

These determine which external data is needed based on the AI’s task. For example:

If a user asks about the weather, the system fetches real-time data instead of relying on old knowledge.
If scheduling a meeting, the system checks calendar availability before responding.

3. MCP Middleware Layer

This acts as a traffic controller, ensuring AI models receive accurate, real-time context before generating responses.

Example: AI Assistant Using MCP

Without MCP (Limited AI)

👤: "Schedule a meeting with Alice tomorrow at 3 PM."

🤖: "I can’t do that." ❌ (No integration with the calendar)

With MCP (Smart AI)

👤: "Schedule a meeting with Alice tomorrow at 3 PM."

🤖 (via MCP): "Let me check your calendar... You’re free at 3 PM. Sending an invite to Alice now!" ✅ (Uses Google Calendar API)

Python Implementation: MCP in Action

Here’s a simple Python implementation showcasing MCP in AI agents:

import openai
import requests

class MCPAdapter:
  def fetch_weather(self, city):
  """Fetch real-time weather data"""
api_key = "your_weather_api_key"
url = f"http://api.weatherapi.com/v1/current.json?key={api_key}&q={city}"
response = requests.get(url)
data = response.json()
  return f"Current temperature in {city}: {data['current']['temp_c']}°C."

class MCPMiddleware:
  def process_query(self, query):
adapter = MCPAdapter()
  if "weather" in query.lower():
city = query.split()[-1]
context = adapter.fetch_weather(city)
  else:
context = "No external data needed."
  return self.query_llm(query, context)

  def query_llm(self, query, context):
openai.api_key = "your_openai_api_key"
prompt = f"User Query: {query}\nContext: {context}\nAI Response:"
response = openai.ChatCompletion.create(
model="gpt-4", messages=[
{"role": "system", "content": "You are an AI assistant."},
{"role": "user", "content": prompt}
]
)
  return response['choices'][0]['message']['content']

# Example Run
if __name__ == "__main__":
mcp = MCPMiddleware()
print(mcp.process_query("What is the weather in London?"))

Expected Output:

Current temperature in London: 15°C.

This demonstrates how MCP fetches real-time weather data before passing it to an LLM, ensuring accurate and contextual responses.

Real-World Use Cases of MCP

1. Financial Assistants: Retrieve live stock prices before providing investment insights.

2. E-commerce Chatbots: Fetch real-time inventory and pricing to help customers make informed purchases.

3. Healthcare AI Agents: Access patient records and medical history securely.

4. Customer Support Automation: Retrieve previous user interactions to provide better service.

MCP is a game-changer for AI applications, enabling LLMs to interact with real-world data rather than relying on static, pre-trained knowledge. By providing standardized ways to integrate AI with external tools, MCP makes AI-powered applications smarter, faster, and more useful.

We hope this clarifies what MCP does.

👉 Over to you: Do you think MCP is more powerful than a traditional API setup?

Thanks for reading!

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At Hashtrust, we specialise in building AI-agent-based solutions using the latest advancements in LLMs, agent-based systems, and intelligent automation. If you’re looking to integrate AI-driven workflows into your business, reach out to us at support@hashtrust.in.

🚀 Hashtrust: Your Partner in Building Cutting-Edge AI Agent-Based Solutions.

Understanding MCP: The USB-C of AI Systems

Introduction