Model Context Protocol (MCP): A Universal Connector for AI and Data

We’ve all experienced the use of AI in our daily coding tasks. Many of us may have used Repl.it, an online code editor with built-in AI capabilities that help generate code and offer guidance. Likewise, tools like GitHub Copilot for VS Code enhance our coding workflow through simple extensions. And if you’ve ever tried Black Box AI or Cursor IDE, you know how inbuilt AI assistance can simplify your work 😉

Imagine if these AI tools could tap into any data source—whether it’s your local files or remote services—without needing custom code for every connection. That’s the promise of the Model Context Protocol (MCP).

What is MCP?

MCP is an open standard that creates secure, two-way connections between your data and AI-powered tools. Think of it like a USB-C port for AI applications—a single, common connector that lets different tools and data sources “talk” to each other.

• For Developers: Instead of writing a custom connector for each new data source, you can build against one standard protocol.
• For AI Tools: With MCP, your AI models can access the exact information they need, no matter where it’s stored.

Why MCP Matter?

As AI assistants become part of our daily workflow (think Replit with its built-in AI or GitHub Copilot for VS Code), the challenge is making sure they have all the context they need. Today, every new data source often requires custom code, which is both messy and time-consuming.

MCP simplifies things by:

• Offering Pre-built Integrations: A growing library of ready-to-use connectors.
• Providing Flexibility: Easily switch between different AI providers.
• Enhancing Security: Best practices ensure your data stays safe within your own infrastructure.

“At Block, open source is more than a development model—it’s the foundation of our work and a commitment to creating technology that drives meaningful change. Open technologies like the Model Context Protocol connect AI to real-world applications in an accessible, transparent, and collaborative way.”
— Dhanji R. Prasanna, CTO at Block

Real-World Impact and Early Adoption

Early adopters—companies like Block and Apollo—are already integrating MCP into their systems. Additionally, development tool companies such as Zed, Replit, Codeium, and Sourcegraph are exploring MCP. This means AI agents can retrieve more relevant information to better understand coding tasks, resulting in higher-quality code with fewer iterations.

How MCP Works: A Simple Breakdown

MCP follows a client-server architecture. Here’s what that means:

• MCP Hosts: These are programs (like Claude Desktop or popular IDEs) that want to access data via MCP.
• MCP Clients: Components that maintain a 1:1 connection with MCP servers.
• MCP Servers: Lightweight adapters that expose specific data sources or tools.
• Local Data Sources: Your computer’s files, databases, and services.
• Remote Services: External systems (like GitHub or Slack) are accessible over the internet.

1. Initialization

• The client sends an initialize request with the protocol version and capabilities
• The server responds with its protocol version and capabilities
• The client sends an initialized notification as an acknowledgement
• Normal message exchange begins​

2. Message exchange

After initialization, the following patterns are supported:

• Request-Response: The client or server sends requests, and the other responds
• Notifications: Either party sends one-way messages

3. Termination

Either party can terminate the connection:

• Clean shutdown via close()
• Transport disconnection
• Error conditions

Watch it in Action

Here's a quick demo using the Claude desktop app, where we've configured MCP:

Watch Claude connect directly to GitHub, create a new repo, and make a PR through a simple MCP integration.

Once MCP was set up in Claude desktop, building this integration took less than an hour. pic.twitter.com/xseX89Z2PD

— Alex Albert (@alexalbert__) November 25, 2024

What Experts Are Saying?

Alex Albert (@alexalbert__) explains it well on X:

• The Challenge:
  “Getting LLMs to interact with external systems isn’t usually that easy. Today, every developer needs to write custom code to connect their LLM apps with data sources. It’s messy, repetitive work.”
• The MCP Solution:
  “MCP fixes this with a standard protocol for sharing resources, tools, and prompts.”
• Key Points of MCP:
  • Unified Architecture: Connects both local (databases, files) and remote (APIs like Slack or GitHub) resources using the same protocol.
  • Beyond Data Sharing: MCP servers can expose not just data, but also tools (like API integrations) and prompts (templated interactions).
  • Built-In Security: Servers control their own resources, eliminating the need to share sensitive API keys with AI providers.
  • Looking Ahead: While MCP is currently supported locally (servers run on your machine), remote server support—with enterprise-grade authentication—is on the horizon.

Get Started with MCP

MCP is designed to be quick to set up. You can have a working integration in under 5 minutes with pre-built servers for platforms like GitHub, Slack, SQL databases, local files, and search engines.

• Step-by-Step Guide:
  Install the Claude Desktop app and follow the instructions on the Model Context Protocol website to connect your first server.
• For Server Developers:
  Learn how to build your own MCP server and contribute to the protocol on GitHub.
• Extra Resources:
  For an in-depth look, check out this blog post by Anthropic and even have Claude explain MCP to you by feeding it the complete documentation available here.

Tools: Enabling AI to Act in the Real World

Tools in MCP allow servers to expose executable functions—imagine them as specialized “action buttons” that AI models can press to perform tasks, run calculations, or interact with external systems. With tools, an AI can not only understand your data but also act upon it, making it a powerful bridge between ideas and execution.

What Are Tools?

• Discovery: Clients can discover available tools using the tools/list endpoint. This lets the AI know what actions it can perform.
• Invocation: Tools are called via the tools/call endpoint. When the AI needs to take action—say, add two numbers or create a GitHub issue—it sends a request to the corresponding tool, and the server carries out the operation.
• Flexibility: Tools range from simple tasks (like a basic calculator) to more complex operations (like integrating with external APIs).

How Are Tools Defined?

Each tool has a unique name, an optional human-friendly description, and a JSON schema that specifies what parameters it expects. For example:

Latest Outcomes

Stay up-to-date with the most recent developments in the Model Context Protocol (MCP):

2025-02-14: Java SDK Released

We’re excited to announce that the Java SDK, developed by Spring AI at VMware Tanzu, is now the official Java SDK for MCP. This new addition joins our existing Kotlin SDK, and the Spring AI team will maintain it as a key part of the MCP community.

2025-01-27: Python SDK 1.2.1

Version 1.2.1 of the MCP Python SDK is here, delivering important stability improvements and bug fixes.

2025-01-18: SDK and Server Improvements

• A simplified, express-like API has been introduced in the TypeScript SDK.
• Eight new clients have been added to our client list.

2025-01-03: More SDK and Server Enhancements

• The FastMCP API is now available in the Python SDK.
• MCP servers have been dockerized in the server repository for easier deployment.

2024-12-21: Kotlin SDK Released

JetBrains has launched a Kotlin SDK for MCP! For a sample MCP Kotlin server implementation, check out this repository.

For full details on these updates, read more here.

Future Developments to Come..

The Model Context Protocol is continuously evolving. Here’s a quick overview of our priorities for H1 2025:

• Remote MCP Support: Enabling secure, remote connections with improved authentication (OAuth 2.0), service discovery, and support for serverless, stateless operations.
• Reference Implementations: Offering comprehensive client examples and a streamlined process for proposing new protocol features to help developers build with MCP.
• Distribution & Discovery: Making MCP servers more accessible through standardized packaging, simplified installation, sandboxing for enhanced security, and a centralized server registry.
• Agent Support:
  Expanding capabilities for complex workflows with better support for hierarchical agent systems, interactive workflows, and real-time streaming of long-running operations.
• Broader Ecosystem:
  Investing in community-led standards, expanding support to new modalities like audio and video, and exploring formal standardization.

Glossary of Technical Terms

• JSON (JavaScript Object Notation): A lightweight, easy-to-read format for exchanging data between systems. It looks similar to a list or a dictionary in everyday language.
• OAuth (Open Authorization): A way for applications to access your information on other websites without giving away your password. Think of it as a valet key for online services.
• API (Application Programming Interface): A set of rules that lets different software applications communicate with each other. It’s like a waiter taking your order and bringing your food—only here, it’s about sending and receiving data.
• Client-Server Architecture: A computing model where a client (like your web browser or an app) sends a request to a server (a powerful computer that stores data) and the server sends back the information or service you need.
• MCP (Model Context Protocol): An open standard that provides a common connector for AI tools and data sources, similar to a USB-C port that lets different devices connect using the same cable.
• OAuth 2.0: The latest version of OAuth, is widely used to safely grant third-party applications limited access to your online accounts without sharing your password.
• Stdio (Standard Input/Output): A way for programs running on the same machine to communicate by reading inputs and writing outputs, similar to how you might use a walkie-talkie with someone nearby.
• SSE (Server-Sent Events): A technology that allows a server to send automatic, real-time updates to a web client over a single connection, much like receiving live news updates on your phone.

Conclusion

MCP is paving the way for a future where AI can seamlessly access any data source—making integrations simpler, more secure, and more efficient. Just as a USB-C port provides a universal way to connect devices like your phone, laptop, and tablet with the same cable, MCP acts as a universal connector for AI tools and data sources.

Imagine how you use familiar connections in everyday life:

• Home Wi-Fi: Just as your router allows all your devices to connect and share information without special cables for each device, MCP allows different AI applications to connect with various data sources using one standard protocol.
• Power Outlets: Think of MCP as a standardized power outlet that lets you plug in any device, no matter where it was made. Similarly, MCP enables AI systems to work with diverse data sources—whether local files, databases, or remote services—without needing unique integration each time.

By replacing a tangle of custom connectors with one universal protocol, MCP is set to become the backbone of smarter, more connected AI systems. This not only simplifies the technical work for developers but also enriches our daily experiences with more powerful, context-aware AI tools.

Shaik Hamzah Shareef

GenAI Intern @ Analytics Vidhya | Final Year @ VIT Chennai
Passionate about AI and machine learning, I'm eager to dive into roles as an AI/ML Engineer or Data Scientist where I can make a real impact. With a knack for quick learning and a love for teamwork, I'm excited to bring innovative solutions and cutting-edge advancements to the table. My curiosity drives me to explore AI across various fields and take the initiative to delve into data engineering, ensuring I stay ahead and deliver impactful projects.