We’ve all encountered this awkward limitation with AI: It can write code or explain complex topics in seconds, but when you ask it to check a local file or run a quick database query, it hits a wall. It’s like having a genius assistant locked in an empty room – smart, but completely disconnected from your actual work. This is where Model Context Protocol (MCP) changes the game. In this article, we will explore MCP in depth.
Table of Contents
MCP Server: AZ of Model Context Protocols
LLMs possess impressive knowledge and reasoning skills, which allow them to perform many complex tasks. But the problem is that their knowledge is limited to their initial training data. This means they can’t access your calendar, run SQL queries, or send email.
It was clear that, to give LLMs real-world knowledge, we had to provide some integrations that would enable them to access real-time knowledge or perform some actions in the real world. This creates classic MXN issues, where developers have to build and maintain custom integrations for each combination of M-Models and N-Tools.
The image below shows the MXN issue correctly:
Function calling (also known as tool calling) provides a powerful and flexible way for OpenAI models to interface with external systems and access data beyond their training data. However, this feature is currently exclusive to OpenAI models, creating vendor lock-in.
This is where MCP steps in. MCP is a write-once, use-anywhere approach to the problem. An app developer can write a single MCP server for any AI system to use a set of tools and data. Similarly, an AI system can implement the protocol and connect to any MCP server that exists today or in the future.
What is MCP (Model Context Protocol)?
MCP is an open source standard, developed by Anthropic for connecting AI applications to external systems.
Using MCP, AI applications such as Cloud or ChatGPT can connect to data sources such as local files and databases, tools such as search engines and calculators, and specialized indicators such as workflows.
Think of MCP as a USB-C port for AI applications. Just as USB-C provides a standard way to connect electronic devices, an MCP provides a standard way to connect AI applications to external systems.
The image below will help you understand MCP Server better:
Architecture of MCP
The model context protocol has a clear structure with components that work together to help the LLM and external systems interact smoothly. An MCP follows a simple client-server architecture, which can be broken down into three simple key components.
MCP host
The host user-facing AI application, is the environment where the AI ​​model lives and interacts with the user. Hosts manage discovery, authorization and communication between clients and servers. This CA is a chat application such as Openai’s Chat GPT interface or Entropic’s Cloud Desktop app, or AIN Enhanced IDEs such as Cursor and Windsurf.
MCP Client
An MCP client is a component within the host that handles low-level communication with the MCP server. MCP clients are instantiated by host applications to communicate with specialized MCP servers. Each client handles a direct communication with a server.
Here, the distinction is important: host applications interact with users, while clients are components that enable server connections.
MCP server
An MCP server is an external program or service that exposes capabilities (tools, data, and so on) to an application. An MCP server can be viewed as a wrapper around some functionality, exposing a set of tools or resources in a standardized way so that any MCP client can request them.
Servers can run locally on the same machine as the host, or run remotely on some cloud service, as MCP is designed to support both scenarios seamlessly.
The image below will help you understand the concept better.
The MCP server may expose one or more capabilities to the client. Capabilities are basically the features or functions that a server makes available.
MCP Server provides the following capabilities:
Tools: Tools are functions that do something on behalf of an AI model. This tool can be used by AI whenever needed. The tools are triggered by the choice of the AI ​​model, which means the LLM (via the host) decides to call a tool when it determines that it needs to perform a certain task. For example: send_email -> send an email to the user
Resources: Resources provide read-only data to the AI ​​model. A resource can be a database record or knowledge base that the AI ​​can query for information, but not modify.
Indications: Indicators are predefined templates or workflows that a server can provide.
Transport layer
The transport layer uses JSON-RPC 2.0 messages to communicate between the client and the server. For this, we mainly have two methods of transportation:
Standard Input/Output (STDIO): Providing fast and synchronous message delivery, ideal for local environments.
Server Cent Events (SSE): Best suited for remote resources, enable efficient, real-time, one-way data transfer from server to client.
How does MCP work?
An MCP gives an AI assistant the ability to securely use external tools, databases, and services. Imagine you ask Claude:
“Find the latest sales report in our database and email it to your manager.”
Step #1 – Tool Discovery
When we launch any MCP client (Cloud Desktop), it connects to your configured MCP servers and asks: “What can I do with the available tools?”
Each server responds with its available tools:
database_query for , for , for , .email_sender for , for , for , .file_browser
Now, Claude knows about their tools.
Step #2 – Understand your need
Claude reads your query and realizes:
He needs to retrieve information he doesn’t have (in this case, he needs to find sales data
database_queryJeezIt needs to perform an external action (send email
email_senderJeez
So Claude plans a 2-step tool setup.
Step #3 – Ask for permission
Before any external action, Cloud Desktop prompts you: “Cloud wants to query your sales database. Allow?”
Nothing goes forward without your approval. This is the core of MCP’s security model.
Step #4 – Querying the Database
Once you grant permission, Cloud sends a structured MCP tool call database_query Server
Next, the server will run a secure database search and return the latest data from the latest sales report. This does not give the cloud direct access to the database.
Step #5 – Sending the Email
Once Claude has the data, Claude prompts another permission: “Claude wants to send an email on your behalf. Approve?”
Once approved, the MCP sends the information email_sender server, and Claude will format the email and deliver it to his manager
Step #6 – Natural Response
Claude wraps everything up nicely and sends you a reply, “Done! I got the latest sales report and emailed it to your manager.”
The whole process usually takes seconds. From your perspective, Cloud only “knows” how to access your database and send emails, but in reality, MCP is intended to be a secure, standardized exchange between multiple systems.
The beauty of MCP is that it transforms AI assistants from isolated conversational tools into true productivity partners that can interact with your entire digital ecosystem, securely and with your explicit permission, every step of the way.
MCP vs Rag
Basically, MCPs and rigs are designed to serve different purposes.
RAG is a technique used to deliver relational knowledge that we have stored in a vector database. In Rig, the user query is converted into vector embedding, which searches through embeddings in the vector database and finds related contexts based on similarity. This relevant context is then provided to the LLM. It’s great for answering questions from large documents like company wikis, knowledge bases, or research papers.
An MCPAI enables models to perform real-world actions with the help of tools. It lets AI integrate with tools and services like databases, APIs, Gmail, calendar, etc.
MCP vs. A2A
The Model Context Protocol (MCP) and the Agent-to-Agent (A2A) protocol are complementary open standards in AI architecture that serve different purposes for how AI agents interact with external systems.
MCP is the standard for how a single AI agent connects to tools, data, and external systems (agent-to-tool communication).
A2A standardizes how multiple, independent AI agents communicate and collaborate with each other (agent-to-agent communication).
Resources
For more information about MCP, you can refer to the official website. ModelContextProtocol.io.
Some amazing MCP servers you can check out:
You can find a list of available MCP servers here:
If you’re interested in learning how to build your own MCP server, check out this detailed course on Hugging Face: https://huggingface.co/mcp-course.
The result
MCP (Model Context Protocol) is an open source standard for connecting AI applications to external systems. With MCP, AI models are not just chatbots, they are fully capable agents that can work with your local files, query your database, send emails with your permission and control.
It also fixed the classic MXN issue. Developers only need to create MCP Server once, then all other AI systems can integrate MCP Server into their application.
MCP is a revolution in how AI systems can interact with the real world. As the MCP ecosystem continues to grow, it will enable AI agents to become more powerful assistants that can reliably and securely operate in diverse environments.