MCP in AI: A Game-Changer for Multi-Agent Communication

“Even the most sophisticated models are constrained by their isolation from data – trapped behind information silos and legacy systems.” – Anthropic, on why context integration matters.

Introduction

AI models like ChatGPT and Claude have come a long way—they generate text, solve problems, and even write code. But they have one significant limitation: they’re isolated from the world of real-time data, tools, and other AI agents. In multi-agent environments, where different AI systems need to work together and share context, this isolation can be a roadblock to true collaboration.

For AI systems to be truly effective, especially in complex environments with multiple agents, they must talk to each other, share information, and work together dynamically. Enter Model Context Protocol (MCP)—a new open standard from Anthropic that’s revolutionizing the way AI agents communicate and collaborate.

MCP allows AI models to break free from their silos and engage in seamless, real-time communication with each other and with external tools and data sources. Think of it as the USB-C for AI—a universal standard for connecting AI models, enabling them to collaborate effortlessly, access real-time data, and complete tasks autonomously.

What is the Model Context Protocol (MCP)?

Model Context Protocol (MCP) is Anthropic’s answer to this problem: an open, standardized protocol that enables real-time, two-way communication between AI models and external systems, as well as multi-agent coordination.

Model Context Protocol (MCP) is an open, standardized protocol that enables AI agents to not only fetch data from external sources, but also interact with each other—sharing context, passing information, and collaborating on tasks. The architecture behind MCP includes:

• Hosts: Applications like Claude Desktop or IDEs that initiate the connection.
• Clients: Components inside the host that maintain the connection.
• Servers: The providers of data, context, and tools—like your files, APIs, calendars, etc.

Together, this setup creates a flexible, persistent context layer that allows AI agents to seamlessly work together—retrieving and sharing data, triggering actions like scheduling meetings, or updating documents—all while maintaining an evolving, dynamic context.

How MCP Transforms Multi-Agent Communication

In today’s world, AI models are often isolated, acting like individual experts but unable to communicate with other systems or AI agents. MCP is here to change that by enabling real-time collaboration across multiple agents. Here’s how it makes a difference:

• AI Agents Working Together

MCP makes it possible for AI agents to communicate and share context in real-time. Imagine a scenario where an AI research assistant summarizes reports while another AI agent drafts insights, and a scheduling bot arranges meetings—all without you needing to intervene or manually copy-paste information. Each agent is aware of the others, working in tandem to complete tasks efficiently.

• Plug-and-Play Integrations for Multiple Tools

Say goodbye to the hassle of writing custom integrations for every new tool. MCP provides pre-built integrations that make it easier for AI agents to connect to your CRM, project management software, or even a calendar, allowing them to coordinate actions and share information effortlessly.

• Freedom to Switch AI Models

MCP is vendor-agnostic, meaning you’re not locked into a single AI provider. Whether you’re using OpenAI, Anthropic, Mistral, or other models, MCP ensures that your agents can still work together, sharing context and collaborating across platforms without compatibility issues.

• Security and Control for Enterprises

MCP comes with built-in security features to ensure sensitive data stays within your infrastructure. With granular access controls and permission systems, you can keep your data secure while enabling your AI agents to interact with each other and external tools in a controlled environment.

With MCP, AI isn’t just a fancy chatbot—it becomes a real teammate that can access the right tools, coordinate with other agents, and get things done without you lifting a finger.

Why Developers and Companies Are Paying Attention?

Initially launched in November 2024, MCP got a modest reception. But in early 2025, it has exploded into the spotlight. It’s now outpacing frameworks like Langchain, and some say it may soon surpass CrewAI and OpenAPI as the go-to standard for building agentic AI systems.

Here’s why it’s taking off:

• Solves Real Integration Pain

MCP simplifies the integration process, eliminating the need to write one-off API connections for every tool. Now, AI models can plug into any MCP-compliant source, whether it’s Slack, GitHub, your database, or your file system. This unified communication is a major step forward for multi-agent collaboration.

• Secure and Controlled

Unlike loose, wild-west plugin ecosystems, MCP ensures that data access remains secure while still enabling agents to interact. You can define access levels, create logs, and set policies that control how your agents communicate, keeping your data safe.

• Persistent Context Layer

Traditional APIs are stateless and disconnected. MCP, on the other hand, enables AI agents to maintain a persistent context—remembering past interactions, tracking what’s been done, and sharing relevant information dynamically across agents. This contextual memory leads to more accurate, relevant, and intelligent outputs.

• Growing Community and Ecosystem

MCP started with Anthropic, but now it’s growing rapidly. Open-source communities, developers, and major AI players are jumping on board. For example, Sourcegraph is using MCP to enable AI-powered code search, and Replit is integrating MCP to help agents access project files seamlessly. The growing ecosystem means more tools and agents will soon be MCP-compatible, unlocking endless possibilities for multi-agent collaboration.

Real-World Use Cases

MCP isn’t just a theoretical improvement—it’s already transforming how AI systems interact with real-world applications. Here are some of the most exciting ways MCP is being used today:

• AI-Powered Customer Support: Imagine a chatbot that doesn’t just pull generic responses but can access live customer data, past interactions, and internal documentation in real time—delivering hyper-personalized support instantly.
• Intelligent Legal Research: A legal AI assistant can instantly retrieve compliance policies, analyze contracts, and summarize case law from secure, permissioned databases—reducing hours of manual research.
• Autonomous Scheduling and Task Management: Instead of manually checking calendars, an AI assistant using MCP can book meetings, adjust schedules, and even notify participants—all while ensuring privacy controls are in place.
• AI-Powered Coding Assistants: Developers can use MCP to have AI agents collaborate, searching for code snippets, modifying files, and accessing resources in real time—boosting productivity and reducing context-switching.
• Smart Educational Tutors: With MCP, AI tutors can pull the latest academic resources, interact with students, and even access a range of educational databases to offer personalized guidance.

From automating workflows to enhancing decision-making, MCP enables AI to truly act as a context-aware, real-time assistant—rather than just a passive text generator.

A Hands-On Experience on Using MCP

Let’s dive into how you can leverage MCP for seamless multi-agent communication using Claude.

1. Install Claude for Desktop

To get started, head over to claude.ai/download and follow the installation instructions for your operating system.

2. Set up your MCP Servers

To access the functionalities, we will use pre-built MCP Servers. 

You can find detailed information about available MCP Servers on the official MCP Github page. Another great way to find such servers is Smithery which is a centralized hub for discovering MCP Servers.

To get started, we need to edit the config file. To configure your MCP Setup, open Claude for Desktop. Navigate to: Claude → Settings… → Developer → Edit Config. This will take you to your claude_desktop_config.json file.

We will be adding MCP Servers related to Browserbase, Google Workspace, Tavily, Postgres and Singapore LTA.

Open the config file in a text editor and replace its contents with the following configuration:

Refer to the individual MCP Server pages for details on setting up authentication and filling in the necessary tokens or credentials.

You will also need Node.js on your computer for this to run properly. To verify you have Node installed, open the command line on your computer.

• On macOS, open the Terminal from your Applications folder
• On Windows, press Windows + R, type “cmd”, and press Enter

Once in the command line, verify you have Node installed by entering in the following command:

If you get an error saying “command not found” or “node is not recognized”, download Node from nodejs.org.

3. Restart Claude

Once your configuration is updated, restart Claude for Desktop.

After restarting, you’ll see a hammer icon in the bottom-right corner of the input box. 

On clicking the hammer tool, you should be able to see a list of tools available to you through your MCP servers integration.

4. Try it out!

You can now interact with Claude and ask questions related to the tools you’ve integrated. It intelligently determines when to invoke the appropriate tool and will prompt you for permission—either for a single action or for the duration of the chat.

I have been able to seamlessly integrate a database, allowing me to query the model effortlessly and receive real-time insights. Beyond that, I can instruct it to send emails about customer delays or schedule events in Google Calendar—streamlining these everyday tasks with remarkable ease. I’ve also connected the Singapore LTA MCP Server to access real-time transportation data, integrated Tavily for instant web search, and enabled Browserbase for seamless browser automation. These additions unlock powerful capabilities—like live traffic monitoring, up-to-date information retrieval, and automated online workflows—all from within a single, unified interface.

Previously, this process would have required me to manually switch between different platforms, write custom scripts for each task, and spend time debugging API connections—all of which slowed down my workflow and made automation cumbersome. Now, with a single plug-in command, I’m connected—everything in one place, fully integrated.

The queries I ran are as below:

• Explain my database tables
• Give me details about city with highest sales

• Check using tavily where the coordinates obtained above belong to?

• Which train should I take for traveling from Marina Bay to City Hall?
• Give me crowding update for NSL line

You can ask the model questions like these—and many more—to instantly retrieve insights, automate tasks, or access real-time information, all in one place.

The Future Is Pluggable

As AI continues to evolve into full-fledged agents capable of reasoning, planning, and acting, they’ll need to break free from their isolated silos. The future of AI isn’t just about smart models—it’s about collaborative, multi-agent systems that work together, share knowledge, and help you get things done.

Just like USB-C unified how we charge and connect devices, MCP is poised to unify how AI connects to the worldl—it’s a paradigm shift in how we build and deploy AI agents. With MCP, we’re moving from isolated intelligence to collaborative, autonomous AI systems that can truly work together and get things done.

If you’re building the future of AI, it might be time to plug in.