I recently experimented with something I believe could reshape how we interact with internal data using AI assistants like ChatGPT, Copilot, and Claude.

It’s called MCP: Model Context Protocol. and it’s a game changer!

What is MCP?

MCP (Model Context Protocol) is an open protocol that enables AI assistants to access real-time, internal data directly from your APIs or databases. It was introduced by Anthropic in November 2024 and is now being adopted across the ecosystem by AI vendors and enterprise developers alike.

Why MCP

Traditional AI systems struggle with accessing live business data and are limited to static information.

MCP solves this by enabling AI assistants to:

• Connect directly to your databases and APIs
• Access real-time, personalized information
• Provide contextual responses based on current data

Example transformation:

• Before (RAG): “How much leave can an employee take each year?”
• After (MCP): “How much leave does Venu have pending this year?”

This shift from static knowledge to contextual insight is exactly what MCP enables.

How does MCP work?

MCP allows AI assistants to send structured requests (in JSON) to a server. The server then returns real-time datafrom internal APIs, services, or databases.

Think of it as a smart middle layer between your internal systems and the AI assistant . Providing relevant, filtered context without exposing the full backend.

Why is MCP useful?

• Works with multiple AI assistants: ChatGPT, Claude, Copilot, and others.
• Reduces manual lookups: The AI gets the latest info instantly.
• Connects modern and legacy systems: MCP can fetch data from old systems too.
• Language-agnostic: You can build an MCP server in .NET, Python, TypeScript, or any language you prefer.
• Simple to build: It’s lightweight and doesn’t require complex infrastructure.

REST API vs MCP (in short)

While REST APIs are designed for general-purpose communication over HTTP, MCP is optimized for AI assistants.

• REST is a developer focused interface.
• MCP is an AI-focused protocol, enabling tools like ChatGPT to fetch just the relevant context in a standardized way, without being tightly coupled to the backend API structure.

And just like REST APIs, you can securely pass API keys or tokens from the client to the MCP server, enabling permission checks and role-based access control.

Security Considerations

Connecting AI assistants to sensitive business data requires robust security measures. MCP implementations must prioritize data protection through:

• Role-based access control (RBAC) : Users only access data relevant to their role
• Token-based authentication: Verify and validate every access request
• API usage monitoring: Track and audit all data interactions
• Granular permissions: Control data access at the field and record level

Proper security implementation ensures AI assistants enhance productivity without compromising data integrity.

Demo Example

Below are screenshots of Claude and Copilot successfully accessing real-time employee data using my sample MCP server:

Screenshot 1: MCP server ​via Claude app

Screenshot 2: ​via Claude app

Screenshot 3: via Copilot

Screenshot 4: via Copilot

Trends: Internet of AI Agents

MCP isn’t just a protocol, it’s a growing ecosystem.

A standout advancement is the introduction of official MCP server templates on GitHub. The Model Context Protocol GitHub repository supplies ready-to-use examples for quick deployment, such as:

• PostgreSQL: For managing real-time structured queries.
• Playwright: To fetch dynamic content from web pages.
• Google Drive: Finding and summarizing documents or retrieving information from spreadsheets.
• E-commerce Solutions: Connects AI to online retail platforms for a variety of shopping tasks like Amazon, Shopify, WooCommerce etc platforms.
• Zapier: Connecting AI agents to over 7,000 applications to automate tasks and workflows.
• Elasticsearch: Delivers powerful search capabilities across vast datasets.

These resources streamline the creation of MCP servers, allowing AI agents to interface with internal tools securely and effectively.

Resources to get started

• Build your own MCP server in C#
  https://devblogs.microsoft.com/dotnet/build-a-model-context-protocol-mcp-server-in-csharp/
• Introduction to Model Context Protocol
  https://modelcontextprotocol.io/introduction

You can build an MCP server in Python, TypeScript, or any language you prefer.

Looking Ahead: Will AI Replace the Browser?

It’s not far-fetched anymore, AI assistants like ChatGPT, Copilot, and Claude are evolving into intelligent agents that can search, summarize, analyze, and even act on our behalf. Instead of manually clicking through tabs and forms, users can simply ask: “What’s the status of Project X?” or “Book me a flight to Delhi under ₹15K.”

With integrations like MCP, these assistants can securely access real-time internal data, just like a browser plugin fetches from a web page, only smarter and more conversational.

We may not eliminate the browser entirely, but we’re certainly shifting away from traditional navigation for many tasks. Especially in enterprise environments, fetching reports or checking dashboards could soon be done via chat.

With OpenAI’s upcoming AI browser expected to change how users interact with information, our MCP based setup could be a strong foundation for exploring AI-driven interfaces at work and beyond.

In this future, the browser becomes a background renderer, while AI takes the front seat as the primary interface between people and information.

Thanks for reading!

Let me know if you’d like help setting up your own MCP server or experimenting with AI+Internal data.

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