The Model Context Protocol (MCP) is an open standard designed to solve a fundamental problem in AI development: how can Large Language Models (LLMs) reliably and securely interact with external tools, data, and services?

It’s the bridge between the probabilistic, non-deterministic world of AI and the deterministic, reliable world of your code and data.

While you could build a custom REST API for your LLM, MCP provides a specialized, standardized “port” for AI-native communication. Think of it as USB-C for AI: a single, well-defined interface for connecting any compliant LLM to any compliant tool or data source.

This guide provides a high-level overview of the protocol itself. We’ll use FastMCP, the leading Python framework for MCP, to illustrate the concepts with simple code examples.

​
Why Do We Need a Protocol?

With countless APIs already in existence, the most common question is: “Why do we need another one?”

The answer lies in standardization. The AI ecosystem is fragmented. Every model provider has its own way of defining and calling tools. MCP’s goal is to create a common language that offers several key advantages:

  1. Interoperability: Build one MCP server, and it can be used by any MCP-compliant client (Claude, Gemini, OpenAI, custom agents, etc.) without custom integration code. This is the protocol’s most important promise.
  2. Discoverability: Clients can dynamically ask a server what it’s capable of at runtime. They receive a structured, machine-readable “menu” of tools and resources.
  3. Security & Safety: MCP provides a clear, sandboxed boundary. An LLM can’t execute arbitrary code on your server; it can only request to run the specific, typed, and validated functions you explicitly expose.
  4. Composability: You can build small, specialized MCP servers and combine them to create powerful, complex applications.

​
Core MCP Components

An MCP server exposes its capabilities through three primary components: Tools, Resources, and Prompts.

​
Tools: Executable Actions

Tools are functions that the LLM can ask the server to execute. They are the action-oriented part of MCP.

In the spirit of a REST API, you can think of Tools as being like POST requests. They are used to perform an action, change state, or trigger a side effect, like sending an email, adding a user to a database, or making a calculation.

With FastMCP, creating a tool is as simple as decorating a Python function.

from fastmcp import FastMCP

mcp = FastMCP()

# This function is now an MCP tool named "get_weather"
@mcp.tool
def get_weather(city: str) -> dict:
    """Gets the current weather for a specific city."""
    # In a real app, this would call a weather API
    return {"city": city, "temperature": "72F", "forecast": "Sunny"}

Learn more about Tools →

​
Resources: Read-Only Data

Resources are data sources that the LLM can read. They are used to load information into the LLM’s context, providing it with knowledge it doesn’t have from its training data.

Following the REST API analogy, Resources are like GET requests. Their purpose is to retrieve information idempotently, ideally without causing side effects. A resource can be anything from a static text file to a dynamic piece of data from a database. Each resource is identified by a unique URI.

from fastmcp import FastMCP

mcp = FastMCP()

# This function provides a resource at the URI "system://status"
@mcp.resource("system://status")
def get_system_status() -> dict:
    """Returns the current operational status of the service."""
    return {"status": "all systems normal"}

​
Resource Templates

You can also create Resource Templates for dynamic data. A client could request users://42/profile to get the profile for a specific user.

from fastmcp import FastMCP

mcp = FastMCP()

# This template provides user data for any given user ID
@mcp.resource("users://{user_id}/profile")
def get_user_profile(user_id: str) -> dict:
    """Returns the profile for a specific user."""
    # Fetch user from a database...
    return {"id": user_id, "name": "Zaphod Beeblebrox"}

Learn more about Resources & Templates →

​
Prompts: Reusable Instructions

Prompts are reusable, parameterized message templates. They provide a way to define consistent, structured instructions that a client can request to guide the LLM’s behavior for a specific task.

from fastmcp import FastMCP

mcp = FastMCP()

@mcp.prompt
def summarize_text(text_to_summarize: str) -> str:
    """Creates a prompt asking the LLM to summarize a piece of text."""
    return f"""
        Please provide a concise, one-paragraph summary of the following text:
        
        {text_to_summarize}
        """

Learn more about Prompts →

​
Advanced Capabilities

Beyond the core components, MCP also supports more advanced interaction patterns, such as a server requesting that the client’s LLM generate a completion (known as sampling), or a server sending asynchronous notifications to a client. These features enable more complex, bidirectional workflows and are fully supported by FastMCP.

​
Next Steps

Now that you understand the core concepts of the Model Context Protocol, you’re ready to start building. The best place to begin is our step-by-step tutorial.

Tutorial: How to Create an MCP Server in Python →