MCP Explained: A Universal Protocol for AI Integrations

Generative AI tools are impressive, but I've long argued that they aren't very useful in the real world unless they have access to more information than just their training data and can actually do something with it. It's this ability that allows AI tools to create usable content, offer useful insights, and perform actions that actually move work forward.

MCP (Model Context Protocol) changes that. It gives AI agents a simple, standardized way to plug into tools, data, and services, no hacks, no hand-coding.

With MCP, AI goes from smart… to actually useful.

What is MCP, Really ?

MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.

While it may sound technical, but the core idea is simple: give AI agents a consistent way to connect with tools, services, and data, no matter where they live or how they’re built.

MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:

A growing list of pre-built integrations that your LLM can directly plug into
The flexibility to switch between LLM providers and vendors
Best practices for securing your data within your infrastructure

Before MCP, each of those actions required a unique API, custom logic, and developer time to glue it all together.

❝

In short: Instead of ad-hoc workarounds, MCP provides a single, real-time protocol designed for autonomous agents.

Subscribe now

How does MCP work ?

Now, let's look at more of the nitty-gritty. MCP operates using a client-host-server model:

The MCP host—typically, a chatbot, IDE, or other AI tool is the central coordinator within the application: it manages each client instance and controls permissions and security policies. Depending on how things are set up, the host may decide to call for something over MCP based on your request or based on an automated process.
The MCP client is initiated by the host and connects to a single server; it handles communications between the host and the server.
The MCP server connects to a data source or tool, either local or remote, and exposes specific capabilities. For example, an MCP server connected to a file storage app can provide capabilities like "search for a file" and "read a file," while an MCP server connected to your team chat app can provide capabilities like "get my latest mentions" and "update my status." Anthropic maintains a list of available MCP servers, or if you're a developer, you can write your own.

MCP servers can provide data using three basic methods:

Prompts are pre-defined templates for the LLM that can be selected by the user through slash commands, menu options, and the like.
Resources are structured data, like files, data from a database, or a commit history that provide additional context to the LLM.
Tools are functions that allow the model to take action, like interacting with an API or writing something to a file.

While MCP might sound superficially similar to how APIs operate, the two differ significantly in design, intent, and flexibility. An API offers a direct, service-specific interface, while MCP is designed to be a unified framework. Many MCP servers use APIs when they're triggered over MCP, so the two often work in tandem, but they're not the same thing.

MCP vs. AI agents

AI agents are AI-powered tools that are able to act autonomously. As a basic example, ChatGPT Deep Research is able to decide what web searches to perform and websites to visit based on your query. You tell it what you want, but it decides how it's going to give it to you.

MCP has the capacity to enable agentic behavior. By allowing developers to connect apps and data sources to AI assistants, they can build AI tools capable of making autonomous decisions and taking action in other apps. But MCP isn't the only way to build AI agents nor does using MCP automatically make any AI-powered tool an AI agent. It's simply a way of connecting an AI to another tool.

MCP is designed primarily for developers building custom integrations and AI applications, it's ideal for teams with technical resources who need to build specialized AI capabilities into their own applications or workflows.

Both approaches have their place in the ecosystem: MCP gives developers deeper control and flexibility for custom implementations, while no-code tools offer accessibility and speed for business users or teams without extensive development resources.

Marketplaces Are Here

Here are the ones to watch:

mcpmarket.com - A plug-and-play directory of MCP servers for tools like GitHub, Figma, Notion, Databricks, and more.
mcp.so - A growing open repo of community-built MCP servers. Discover one. Fork it. Build your own.
Cline’s MCP Marketplace - A GitHub-powered hub for open-source MCP connectors anyone can use.

How to get started with MCP

If you're a developer building an AI app and want it to be able to initiate tasks in other apps not just access data then MCP is well worth a look. It gives your AI tools the ability to trigger actions in external systems, like sending messages, creating records, or kicking off workflows, all through a standardized protocol. And if you don't want to create your own server, you can find a list of available MCP servers here.

For more Deep Dive

Here are some great places to explore MCP further:

Introducing the Model Context Protocol by Anthropic
Model Context Protocol on GitHub

Hope you enjoyed this deep dive into “MCP Explained” and how it connects AI to your data and tools ? Hit a like ❤️For more simple explanations, useful insights on coding, system design, and tech trends, Subscribe To My Newsletter! 🚀