MCP (Model Context Protocol) is like a universal language that lets an AI (such as ChatGPT or Claude) talk to different apps, databases, and tools - without needing a separate “translator” for each one.

Think of it like USB for AI - you can plug in any compatible device and it just works, without needing a different cable for each gadget. MCP is the “plug” that allows AI systems to connect with external tools and data sources.

MCP Has Two Conceptual Layers

• Data layer: Uses JSON-RPC 2.0 to define message formats, lifecycle events, and core primitives (tools, resources, prompts).
• Transport layer: Handles how messages are sent - e.g. via standard I/O for local use, or HTTP + streaming for remote use.

MCP Server

An MCP server is like a shop that offers many tools and pieces of data you can use.
It provides things you can request - such as:

• “Give me this file,”
• “Search this database,”
• “Run this function.”

When you send a request through MCP, the server performs the action and sends the result back. The server also tells you which tools it supports.

In other words:

An MCP server is a program that exposes capabilities to clients via MCP.

It offers:

• Tools: functions or operations that the client/AI can call (e.g. “search database,” “send email,” “run calculation”).
• Resources: read-only data objects (like files, APIs, records) that clients can fetch or query.
• Prompts: templated instructions or workflows that the AI can invoke or refine.

The server handles incoming MCP messages (requests) from clients, executes them (within permitted scope), and sends back structured results.

Servers can run locally (on the same machine) or remotely, using different transports (stdio, HTTP, streaming) depending on the use case.

MCP Client

An MCP client is like the user or messenger that talks to the server. It sends requests - “run this,” “fetch that” - using the MCP “language,” and then receives and passes back the results.

The client figures out what the server can do (which tools or data it offers) and manages the communication over the chosen transport.

Its main roles:

• Negotiates protocol version and capabilities with the server so both sides agree on supported features.
• Discovers which tools, resources, or prompts the server offers.
• Sends requests (method calls) and receives responses over JSON-RPC.
• Optionally offers features that the server can request from the client (e.g. sampling, prompting, logging).

Each client is dedicated to communicating with one server, but a host application can manage multiple clients to combine capabilities from several servers.

How They Work Together

• The host application (for example, a chatbot interface or AI agent controller) embeds one or more MCP clients.
• Each MCP client connects to an MCP server, negotiates features, and acts as a bridge for that connection.
• The MCP server offers tools and resources the AI can use — the client requests them, the server responds, and the AI uses the results in its reasoning.

This separation allows modularity: you can change or upgrade servers or clients independently, as long as they follow the MCP specification.

In Short

Model Context Protocol (MCP) defines a standard language for AI systems to talk to external tools or data sources.

• The MCP server hosts and offers tools, data, and prompts.
• The MCP client connects and interacts with the server using the protocol.
• The host application uses the client to let the AI access those external capabilities — cleanly and consistently.