What is Model Context Protocol (MCP)?
If you've been reading about AI tools lately, you might have come across the term Model Context Protocol (MCP). At first glance, it sounds technical and confusing. But don't worry — let's break it down in the simplest way.
Think of MCP as a universal language that lets AI apps talk to other tools and systems safely.
Today, different AI apps use different formats to access data and perform actions. One AI might know how to fetch your calendar, another might know how to search Google Docs, and another might know your local files — but they don't follow one standard.
MCP solves this.
Model Context Protocol is a standard way for AI tools (like ChatGPT, Claude, or custom bots) to connect to external apps, APIs, databases, and even your own computer — in a secure and structured format.
In short:
- MCP helps AI tools interact with real-world systems in a predictable way.
- It acts like a translator between AI and external services.
What is an MCP Server?
To understand MCP fully, imagine this scenario:
You want your AI chatbot to be able to:
- Read a file from your laptop
- Look up an email
- Fetch CRM customer details
- Check booking status from a travel API
Your AI doesn't know by default how to do these things. So we create something called an MCP Server.
An MCP Server is like a service that provides tools to the AI. It exposes operations that the AI can use — such as:
| What you want | Tool exposed by MCP Server |
|---|---|
| Search in files | read_file, search_text |
| Database lookup | find_customer |
| Control IoT device | start_device, stop_device |
| API access | get_tickets, create_order |
So the MCP Server acts like a toolbox.
Types of MCP Servers
There are two types of MCP Servers, each serving different needs:
1. Local MCP Server
Runs on your computer
Example use cases:
- Let ChatGPT read your local files
- Let an AI fill spreadsheet data on your PC
- Run commands or scripts securely using AI
🔒 Never exposes your data to the internet — the AI accesses only whatever tools you allow.
2. Remote MCP Server
Runs on the internet / cloud
Example use cases:
- Connect AI to your CRM
- Connect AI to ticketing system like JIRA
- Connect AI to company APIs
🌐 Multiple users/apps can connect to the same remote MCP Server.
Want to try a Remote MCP Server? Test Remote MCP Server →
What is an MCP Client?
If MCP Server is the toolbox, then the MCP Client is the AI app that uses that toolbox.
An MCP Client is the software that:
- Discovers what tools the MCP Server offers
- Sends instructions
- Receives results and responses
Examples of MCP Clients:
- ChatGPT app using MCP plugins
- Claude Desktop app
- Custom AI chatbot built by developers
- Internal company AI assistant
Basically:
- MCP Client = The AI app that wants to use tools
- MCP Server = The service that provides the tools
Want to try an MCP Client? Test MCP Client →
In Summary
| Concept | Meaning (simple) |
|---|---|
| MCP | A standard that helps AI talk to other systems |
| MCP Server | A toolbox of actions AI can perform |
| MCP Client | The AI app that uses those actions |
When these work together:
- AI becomes more useful
- Workflows become automated
- Data stays secure (with full user control)
Why is MCP important?
Until now, AI models mostly worked inside a sandbox — they could answer based on training data but couldn't interact with your world.
With MCP, AI can now:
- Read and update information
- Trigger actions
- Connect to thousands of apps
- Become a real personal or business assistant
This is the future.
Nikhil Tiwari
15+ years of experience in product development, AI enthusiast, and passionate about building innovative solutions that bridge the gap between technology and real-world applications. Specializes in creating developer tools and platforms that make complex technologies accessible to everyone.