#Technology #AI Tools #Claude 26 Apr. 2025

Model Context Protocol (MCP) Explained

By Kobi Shmueli

Every few years, a new standard emerges that quietly reshapes the future-HTTP, SMTP, OAuth. In 2024, we may have just met the next one: MCP, or Model Context Protocol. Created by Anthropic, MCP promises to make large language models (LLMs) like Claude more capable by standardizing how they interact with tools and data. In this article, we’ll break down what MCP is, why it matters, and where the real opportunities lie.

I remember the first time I asked an LLM to send an email. It answered brilliantly-but couldn’t actually do anything. That was the moment I realized: intelligence without action is just performance. MCP changes that.

What Is MCP?

MCP (Model Context Protocol) is an open standard that lets AI agents-powered by LLMs-call external tools and APIs in a consistent, scalable way. It acts as a universal translator between an LLM and the real world.

Instead of hardcoding integrations, MCP lets developers expose tools as "MCP servers," which can be called over the network by any MCP-compatible client (like Claude or Cursor). Whether it’s fetching a database record, sending an email, or running a script, MCP makes the call seamless.

Visual flow chart:

Why IS MCP a Big Deal

As Professor Ross Mike explains, LLMs on their own are just prediction engines. They can suggest text but can't take action. Tools make LLMs useful-but integrating them is a nightmare. Every tool speaks a different language.

MCP changes that by introducing a standard interface between models and services. Imagine the difference between talking to five friends who each speak a different language vs. everyone agreeing to use English. MCP is that lingua franca.

"It’s not about making LLMs smarter. It’s about making them useful." - Professor Ross Mike

How MCP Works

The ecosystem consists of four parts:

MCP Client - the LLM-facing interface (e.g., Claude, Cursor).
MCP Protocol - the standardized communication layer (built on JSON-RPC).
MCP Server - wraps your tool and exposes it to AI agents.
Service - your actual API, database, or functionality.

Visual Flow chart:

This architecture lets LLMs call your service as if it were a native function.

Real-World Examples

Code tools like Replit and Cursor use MCP to give AI access to codebases.
Enterprise apps use it to hook LLMs into CRMs, databases, and ticketing systems.
Security tools like Semgrep expose vulnerability scanners as MCP tools.

Case Study Highlight: At Tempo, an AI productivity startup, engineers connected their LLM to a helpdesk system via MCP. Instead of juggling integrations, they deployed a lightweight MCP server for customer tickets. Now, AI agents can autonomously summarize, assign, and escalate tickets without human intervention-saving hours every day.

Access Control & Challenges

MCP is powerful-but not opinionated. There’s no built-in authentication, so developers must add:

API key or JWT-based auth
Role-based access control
Network and rate-limiting policies

Additionally, the tooling isn’t yet beginner-friendly. Setting up an MCP server requires command-line work, config file management, and sometimes local processes.

Opportunities for Builders

Every new protocol spawns an ecosystem. Here’s what early adopters can build:

MCP App Store - deployable MCP tools with one-click hosting.
DevX Tools - GUIs and CLIs to streamline MCP server setup.
Enterprise Adapters - MCP wrappers for tools like Notion, Slack, or Salesforce.
Security Layers - logging, access control, and usage analytics.

This is a foundational layer-think HTTPS for agents.

Final Thoughts

LLMs won’t reach their full potential until they can act reliably and securely. MCP is a major step toward that future. While it’s still early days, the standard shows signs of becoming the universal way for AIs to access tools. If that happens, early builders will have a front-row seat to the next wave of agent-native applications.