As Large Language Models (LLMs) become more deeply embedded in business workflows, one persistent limitation remains: their inability to access real-time, external context. Relying solely on training data leaves models outdated, often resulting in incomplete or inaccurate outputs.

That’s where the Model Context Protocol (MCP) steps in.

Developed by Anthropic, MCP is an open-source AI model context protocol designed to standardize how LLMs interact with tools and external data—securely, consistently, and in real-time. Whether you’re trying to build an MCP server for your app or exploring MCP use cases for enterprise AI, this protocol aims to change how developers and organizations integrate intelligence into their products.

Bridging the gap: Why MCP matters

Modern AI applications need more than smarts—they need situational awareness. From live dashboards to dynamic chatbots, the demand for real-time AI is everywhere.

Before MCP, developers had to create custom connectors for every new tool or data source. It was repetitive, brittle, and hard to scale. MCP eliminates that friction by providing a unified interface for tool discovery, context retrieval, and action execution.

The result? AI systems that are no longer static responders but adaptive agents—capable of evaluating and using external resources on the fly.

MCP vs Traditional API’s

Before and after MCP 

So, how does MCP actually work?

Think of MCP as a client-server architecture for intelligent systems. The client (host)—often a tool like Claude or an AI-powered app—initiates the connection. The MCP server provides access to external data, tools, and context.

By following the MCP specification, developers can reliably build MCP clients and servers that speak the same language, resulting in consistent interactions between models and external systems.

This setup ensures your AI not only "knows" things but also does things.

General architecture

At its core, MCP follows a client-server architecture where a host application can connect to multiple servers:

• MCP hosts: Programs like Claude Desktop, IDEs, or Al tools that want to access data through MCP
• MCP clients: Protocol clients that maintain 1:1 connections with servers
• MCP servers: Lightweight programs that each expose specific capabilities through the standardized Model Context Protocol
  

Core concepts in MCP

1. Tools

Tools let LLMs execute actions. Think of them as functional plugins.

@mcp.tool()
async def conversion(input_unit: str, output_unit: str, value_to_convert: float) -> str:
   """Perform a conversion between two units.


   Args:
       input_unit: The unit of the value to be converted.
       output_unit: The desired unit after conversion.
       value_to_convert: The numerical value to convert.
   """
   if input_unit == "UNIT_A" and output_unit == "UNIT_B":
       # Perform specific conversion logic from UNIT_A to UNIT_B
       converted_value = value_to_convert * SOME_FACTOR
       result = f"{value_to_convert} {input_unit} is equal to {converted_value} {output_unit}"
   elif input_unit == "UNIT_C" and output_unit == "UNIT_D":
       # Perform specific conversion logic from UNIT_C to UNIT_D
       converted_value = value_to_convert / ANOTHER_FACTOR
       result = f"{value_to_convert} {input_unit} is equal to {converted_value} {output_unit}"
   else:
       result = f"Conversion from {input_unit} to {output_unit} is not supported."
   return result

These tools are callable by AI to perform real-world tasks, like calculations or API calls.

2. Resources:

Resources supply contextual data—text files, PDFs, logs, or binary content—that the model can read during execution.

# resource template expecting a 'user_id' from the URI
@mcp.resource("db://users/{user_id}/email")
async def get_user_email(user_id: str) -> str:
   """Retrieves the email address for a given user ID."""
   # Replace with actual database lookup
   emails = {"123": "alice@example.com", "456": "bob@example.com"}
   return emails.get(user_id, "not_found@example.com")

3. Prompts:

Prompt templates enable reusable content generation strategies.

@mcp.prompt()
def ask_review(code_snippet: str) -> str:
   """Generates a standard code review request."""
   return f"Please review the following code snippet for potential bugs and style issues:\n```python\n{code_snippet}\n```"

Use these to structure consistent, intelligent responses.

4. Roots

Roots define the operational scope—e.g., what directories or endpoints the MCP server can use.

from fastmcp.client.transports import SSETransport
import asyncio
transport_explicit = SSETransport(url="http://localhost:8000/sse")
async def client_example():
   # Connect to a running MCP server and specify the roots we want to use
   async with Client(
       transport_explicit, roots=["file:///home/user/projects/"]
   ) as client:

They help maintain guardrails and enforce context boundaries.

5. Sampling

Sampling allows the server to delegate text generation to the client’s LLM, while keeping business logic centralized.

@mcp.tool()
async def generate_content(topic: str, context: Context) -> str:
   """Generate content about the given topic."""
   # The server requests a completion from the client LLM
   response = await context.sample(
       f"Write a paragraph on the given {topic}",
   )
   return response.text

6. Transports

Transports are the messaging layer. MCP uses JSON-RPC over protocols like stdio and SSE.

from fastmcp.client.transports import (
   SSETransport,
   StdioTransport
)

Try it yourself: MCP Inspector

Once MCP server is running we can inspect it through Inspector available on local host 

Building with MCP: FastMCP SDK

The easiest way to get started is with FastMCP, the official Python SDK.

It helps you:

• Build an MCP client or server
• Register tools, resources, and prompts
• Handle protocol communications without boilerplate

FastMCP supports stdio and Server-Sent Events (SSE) out of the box, and it's actively maintained.

Explore the MCP documentation for examples and advanced setups.

Real-world example: Online travel agency

Let’s look at one of the more compelling MCP use cases in action.

The problem

A mid-sized travel agency wanted to improve its AI assistant. Customers asked questions like:

• “Is there a hotel available in Tokyo tonight?”
• “What’s the weather like in Paris this weekend?”

Their existing system couldn’t respond accurately—it lacked live data access.

The solution

Using MCP, the agency created a setup where the AI could:

• Autonomously discover tools and APIs
• Evaluate tool relevance for each query
• Pull real-time weather, flight, and accommodation data

The impact

The assistant became a context-aware decision-maker. Customers got relevant answers in seconds—without human intervention. The company saw improved satisfaction and a drop in support tickets.

This is the power of MCP for LLM integrations done right.

The road ahead

The ModelContextProtocol GitHub repo is where development is actively happening. Expect ongoing contributions in:

• Tool and platform integrations
• Security enhancements
• Developer experience improvements

Whether you're just exploring or ready to build an MCP server for production, now is a great time to get involved.

Final thoughts

MCP isn’t just another API—it’s an operating layer for intelligent agents. If you're serious about building LLM applications that are flexible, scalable, and smart, mastering the model context protocol will give you a massive edge.

Start small. Build. Contribute. And let your AI agents do more—with less.

At KeyValue, we’re already exploring ways to integrate MCP across real-world use cases—helping our partners build smarter, more adaptive AI systems.