What is MCP?

Definition and basic overview

Model Context Protocol (MCP) is a standard method for connecting AI applications with external systems. It can be a database, function, tool (e.g., search the web, create PDFs), or any process that helps AI better understand context and perform tasks. If you haven’t realized it, most AI products already have this integration. A great example: when you ask something in ChatGPT or Grok, sometimes you’ll see a background process called "search the web." That’s MCP behind the scenes.

Why you should learn it

Today, anyone can create a small app to speed up daily tasks with AI. However, results often fall short because the app lacks full context about the question. What if you could give the AI complete information from your database? That’s where MCP comes in. It helps AI better understand context before generating responses. The AI can execute available tools in the MCP and use the results to produce more accurate, context-aware outputs.

Architecture of MCP

Key participants

There are 3 key participants of MCP, which are:

MCP Host: The AI application that hosts, manages, and coordinates one or multiple MCP servers. It is responsible for deciding when to request external context and which tools should be used.

MCP Server: The service that exposes tools and context. It defines available capabilities, executes requests, and returns structured results back to the host.

MCP Client: The component that maintains the connection to an MCP server. It handles communication, sends requests, receives responses, and forwards context to the MCP host.Key participants

Building Blocks for MCP

Beyond the key participants, MCP relies on several core building blocks. These components work together to make tool execution reliable, secure, and context-aware. Not every MCP server needs all of them, but production-ready systems usually implement most of these blocks.

Tool Definitions

Tool definitions describe what the MCP server can do. Each tool clearly specifies:

• The tool name
• What the tool does
• Required input parameters
• The expected output structure

These definitions allow the AI to reason about which tool to call, when to call it, and how to use the result. Well-designed tool definitions are critical—ambiguous or overly complex tools often lead to poor AI decisions.

Good tool definitions are:

• Explicit and predictable
• Narrow in responsibility
• Deterministic in output

The clearer the definition, the better the AI can use it.

Transport Layer

The transport layer defines how the MCP client and server communicate. It handles message delivery, connection lifecycle, and data streaming.

1. stdio (Standard Input/Output)

This method uses standard input and output streams for communication. The MCP server runs as a child process and communicates via stdin/stdout.

Best suited for:

• Local development
• CLI-based tools
• Single-user environments

It’s simple, fast, and easy to debug, making it ideal for early-stage MCP development.

2. SSE (Server-Sent Events)

SSE enables one-way, real-time communication from server to client over HTTP. The server can push updates whenever new data is available.

Best suited for:

• Remote MCP servers
• Web-based clients
• Long-running or streaming tasks

Because SSE is built on standard web protocols, it works well with existing infrastructure and firewalls.

Context Injection

Context injection provides relevant background information to the AI before it generates a response. This may include:

• User profile data
• Recent conversation history
• Database records
• Application state

Instead of forcing the AI to guess, MCP allows you to explicitly supply the information it needs. Strong context injection dramatically improves accuracy, relevance, and consistency.

Designing good context is often more important than model choice. Poor context leads to hallucinations; good context leads to reliable outputs. You can learn more on how to build better context injection at https://context7.com/.

Security & Permissions

Security controls who can access which tools and data. This is essential when MCP is connected to sensitive systems such as:

• User databases
• Financial data
• Internal APIs

Common security mechanisms include:

• Authentication (API keys, tokens, OAuth)
• Authorization (role-based or permission-based access)
• Tool-level restrictions

A best practice is to treat MCP tools as privileged operations and only expose what is strictly necessary.

Observability & Error Handling

Observability helps you understand what the MCP system is doing at runtime. This includes:

• Tool invocation logs
• Execution latency
• Success and failure rates
• Error messages and stack traces

Proper error handling ensures that:

• Failures are surfaced clearly to the host
• Partial failures don’t crash the system
• The AI can recover or retry when possible

Without observability, MCP systems become difficult to debug and unsafe to scale.

Helpful Resources

If you want to go deeper and start building with MCP, the following resources are a great place to begin:

Getting Started Guide

Learn the fundamentals of MCP, its design philosophy, and how to set up your first MCP server.

👉 https://modelcontextprotocol.io/docs/getting-started/intro 1.

Official Examples

Explore real-world MCP implementations and see how tools, transports, and context are put together.

👉 https://modelcontextprotocol.io/examples 1.

Community Discussions & Support

Ask questions, share ideas, and learn from others building with MCP.

👉 https://github.com/orgs/modelcontextprotocol/discussions