When Anthropic released the Model Context Protocol in late 2024, I initially dismissed it as an internal Anthropic thing. A few months later, it had become one of the most talked-about standards in AI development circles.
Here's what it actually is and why it's worth paying attention to.
The Problem MCP Solves
Every AI application that needs to interact with external systems — a database, a file system, a calendar, a CRM — has to build that integration from scratch. If you're building a Claude-powered coding assistant and you want it to read your GitHub repos, you write GitHub integration code. If you then want it to access your Jira tickets, you write Jira integration code. And if you switch from Claude to another model, you potentially rewrite everything.
This is the "N×M problem": N AI applications × M tools = N×M integrations to build and maintain.
MCP is an attempt to standardize how AI systems connect to external tools and data — so you build the integration once (as an MCP server) and any compatible AI client can use it.
How It Works: The Basic Architecture
MCP has three components:
MCP Hosts — AI applications that want to use external tools. Claude Desktop is the primary example, but any application can implement the MCP client protocol.
MCP Clients — Libraries within the host application that handle communication with MCP servers.
MCP Servers — Programs that expose tools or data through the MCP protocol. You could build an MCP server for your company's database, your file system, your calendar, your CRM — anything.
┌─────────────────┐ MCP Protocol ┌──────────────────┐
│ Claude Desktop│ ◄─────────────────────────► │ MCP Server: │
│ (MCP Host) │ │ GitHub, Files, │
│ │ ◄─────────────────────────► │ Database, etc. │
└─────────────────┘ └──────────────────┘
When you ask Claude to "look at my recent GitHub commits," Claude Desktop communicates with a GitHub MCP server through the protocol, the server fetches the data, and the results come back to Claude in a standardized format.
What MCP Servers Can Expose
MCP servers can provide three types of things:
Tools — Functions the AI can call to take actions. Examples:
create_file(path, content)— create a file on disksend_email(to, subject, body)— send an emailrun_query(sql)— run a database querysearch_web(query)— search the internet
Resources — Data sources the AI can read. Examples:
- Files and directories on your file system
- Database contents
- API responses
- Application state
Prompts — Pre-defined prompt templates that can be invoked by users. Useful for standardizing common workflows.
Why This Matters
For users: If you use Claude Desktop, you can connect it to your local file system, your databases, your APIs — and Claude can read from and act on those systems directly, within whatever permissions you grant. It's the difference between a Claude that knows what's in a text box and a Claude that can actually interact with your systems.
For developers: Build your integration once as an MCP server, and any MCP-compatible AI client can use it. You're not locked into specific AI vendors, and you don't rebuild integrations when you switch models.
For the ecosystem: An open standard means the community can share MCP servers. Already there are community-built servers for Slack, GitHub, PostgreSQL, Google Drive, file systems, web browsers, and more.
The Security Model
One thing worth understanding: MCP servers run locally (for local integrations) or as remote servers with explicit authentication. Claude Desktop asks your permission before connecting to MCP servers, and each server declares exactly what it can access.
This matters because of prompt injection concerns. If an MCP server can execute arbitrary code on your machine, and an AI assistant is processing untrusted content (like a web page), that's a potential attack vector. The MCP spec includes guidance on this, and the community is actively working on security standards.
Before connecting any MCP server to your AI assistant, understand what that server can access and do. Don't connect a server with file system write access unless you trust both the server and the AI assistant's judgment about when to write files.
Getting Started with MCP
As a user (Claude Desktop):
- Install Claude Desktop
- Edit the MCP configuration file to add servers you want
- Restart Claude Desktop
- Claude will now have access to those tools
Pre-built servers are available for common tools at the MCP GitHub organization.
As a developer (building an MCP server):
Anthropic provides Python and TypeScript SDKs. A minimal server looks like:
from mcp.server import Server
from mcp.server.stdio import stdio_server
import mcp.types as types
app = Server("my-server")
@app.list_tools()
async def list_tools() -> list[types.Tool]:
return [
types.Tool(
name="get_weather",
description="Get current weather for a location",
inputSchema={
"type": "object",
"properties": {
"location": {"type": "string", "description": "City name"}
},
"required": ["location"]
}
)
]
@app.call_tool()
async def call_tool(name: str, arguments: dict):
if name == "get_weather":
# Fetch weather data
weather = fetch_weather(arguments["location"])
return [types.TextContent(type="text", text=weather)]
async def main():
async with stdio_server() as streams:
await app.run(*streams, app.create_initialization_options())
That's the basic pattern: declare what tools you expose, implement the logic, and the MCP protocol handles communication with AI clients.
Is MCP a Big Deal?
Honest take: the concept of AI connecting to external tools isn't new — OpenAI has had function calling and plugins, LangChain has integrations, etc. What MCP adds is standardization and openness.
Whether that standardization takes hold depends on adoption. The fact that Anthropic open-sourced it and the community quickly built servers for dozens of popular tools is a positive signal. The growing list of companies building MCP-compatible products suggests it's gaining real traction.
For developers building AI systems that need external integrations, it's worth at least understanding what MCP is. If you're already building custom integrations, it may be worth evaluating whether MCP-compatible servers would reduce your maintenance burden.
For users of Claude Desktop, MCP is what lets you do genuinely powerful things — giving Claude access to your actual files, databases, and tools, rather than just what you manually paste into a chat window.
