The Model Context Protocol (MCP): AI’s Universal Translator

If you know blockchain, you already understand oracles. Oracles are services that fetch external data to be used by decentralized systems. MCP serves a similar purpose for AI applications — it’s the messenger that connects AI models with the external tools they need.

What is MCP?

MCP is a tool-calling protocol for LLMs. Think of it as the USB-C of AI connectivity — a standardized way for AI applications to plug into any external tool, data source, or service.

Traditionally, if we wanted our AI model to access different services, we’d need custom API integrations for each one. While workable, this approach creates serious friction, scalability issues, and maintenance headaches. MCP eliminates this by standing between your AI software and the tools it needs, unifying all different APIs into one consistent interface.

Why Does This Matter?

For Users:

No more frustration when your AI assistant can’t access the tools you use daily. With MCP, your preferred AI app can seamlessly work with your data and services.

For Developers:

Instead of building and maintaining countless custom integrations, you implement the MCP standard once. Suddenly, your application can connect to any MCP-compatible server — today’s and tomorrow’s.

For Companies:

Better user experience means higher retention and attraction, which translates to profitability and reputational gains. Your AI investments become future-proof.

MCP Architecture: Three Simple Components

1. The Host(any application

This is your AI application (lets think Claude Desktop, ChatGPT, Cursor, or any custom AI agent. The Host is what users interact with directly.the client is inside the client

2. The Client

Consider this the universal connector. The Client lives inside the Host and manages communication with external servers. Each Client connects to one Server, handling all the protocol details so the Host doesn’t have to.

3. The Server

Servers are external programs that expose specific capabilities — whether that’s accessing a database, calling an API, or executing specialized tools. They’re lightweight wrappers that make existing functionality available to AI models in a standardized way.

How It Works: A Complete Flow

Let’s trace a complete request through the system:

1. User Request: “Get Q1 sales data” → Host (Claude)
2. Analysis: Host’s LLM analyzes the request and determines it needs sales data
3. Tool Selection: LLM decides to use the get_sales_data tool from the SalesDB Server
4. Invocation: Host → Client: “Call get_sales_data with quarter=Q1"
5. Protocol Call: Client → Server: Sends standardized MCP tools/call request
6. Execution: Server queries the database, processes the data (the actual work happens here)
7. Response: Server → Client: Returns raw sales data
8. Interpretation: Client → Host’s LLM: Passes the data for cognitive processing
9. Final Answer: LLM analyzes, summarizes, and explains: “Q1 sales were $2.4M, up 15% from last quarter…” → User

The key insight? The LLM does the thinking, but the Server does the doing. The LLM orchestrates and interprets, while specialized Servers handle the actual operations.

Example 2 for mcp

figma make

Figma Make acts as the MCP Host and instantiates multiple MCP clients: one client communicates with the Figma MCP Server over a streamable HTTP (SSE) transport, while another client communicates with a local filesystem MCP Server over STDIO. The Host orchestrates both servers to support redesigning an uploaded design file.

Communication Protocol?

mcp uses the json rpc protocol for transportaion,in which it three types of messages:

1. Requests:

conytains id , method nd parameters

2. Responses

cobntains the required id and Either a result (for success) or an error (for failure)

3. Notifications

One-way messages that don’t require a response. Typically sent from Server to Client to provide updates or notifications about events.”Processing data…”

MCP Capabilities

MCP Servers expose a variety of capabilities to Clients through the communication protocol. These capabilities fall into four main categories, each with distinct characteristics and use cases.

tools

prompts

resources(can be docs)theyre read only

Types of MCP Servers

MCP Servers come in three flavors:

• Tool Servers: Provide executable actions (APIs, commands, functions)
• Resource Servers: Offer access to data sources (files, databases, streams)
• Combined Servers: Deliver both tools and resources

Flexibility by Design

What makes MCP powerful is its flexibility:

• Servers can be written in any programming language
• Communication happens via HTTP or stdio (standard input/output)
• All implementations follow the same MCP specification

This means your Python data science tools, your Go microservices, and your JavaScript web APIs can all become MCP Servers, instantly available to any MCP-compatible AI application.

MCP SDK

ready made like components for starting out with mcp which handles all the low levels of developemnt leaving the developer to handle the other techicals part of their mcp, they exists in different programming langauges

The Big Picture

MCP transforms the AI ecosystem from a tangled web of custom integrations into a clean, modular architecture. Instead of every AI application needing separate connections to every tool (an M×N problem), we now have a simple M+N solution: M applications plus N Servers, all speaking the same language.

Whether you’re building AI applications, creating tools for AI to use, or simply wanting your AI assistant to work better with your existing tools, MCP represents a fundamental shift toward interoperability, simplicity, and power in the age of intelligent software.