Introduction

Large language models (LLMs) are powerful. They can explain concepts, reason through problems, and generate text on almost any topic. But here’s the limitation: they don’t actually know anything about your world. They can’t look into your company’s database, check today’s logs, or update records in your systems. On their own, they’re intelligent—but disconnected.

That’s where Model Context Protocol (MCP) comes in. MCP gives agents a standard way to connect to real systems and data. It defines how agents (the clients) can talk to servers that expose tools, files, or APIs. With MCP, agents stop being just chat interfaces and start becoming useful in real workflows.

Reference: ModelContextProtocol.io

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How LLMs, Agents, MCP, and Systems Fit Together

To understand MCP, it helps to see how all the parts relate:

User → Agent → LLM
            ↓
       MCP Client → MCP Server → Systems/APIs → Data

• User: interacts with the agent in natural language.
• Agent: the controller. It decides when to call the LLM for reasoning and when to call MCP for actions.
• LLM: the reasoning engine. It generates text, answers, or plans when the agent needs it.
• MCP Client: the agent’s role inside MCP. It makes requests.
• MCP Server: the system’s role inside MCP. It exposes tools and data.
• Systems/APIs: existing software and services. They provide the actual functions.
• Data: the information stored in those systems.

In short: the agent calls the LLM for intelligence, and it uses MCP clients/servers to connect with real systems and data.

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The Agent as MCP Client

In MCP, the roles are clear:

• The agent (MCP client) makes requests: “What can you do? Can you fetch this file? Can you run this query?”
• The MCP server responds: “Here are the tools I support. Here are the results you asked for.”

This setup is powerful because you don’t have to build one-off integrations for every new agent or system. If something speaks MCP as a server, any MCP-enabled agent can use it.

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Why This Matters

Without MCP, an agent can only rely on what it was trained on or a handful of hardcoded APIs. That means:

• It can explain SQL syntax, but it can’t actually run a query.
• It can describe how to check logs, but it can’t retrieve them.
• It can suggest a workflow, but it can’t carry it out.

With MCP, the agent can:

• Connect to your database and pull live data.
• Fetch today’s logs and summarize them.
• Execute workflow steps across different systems.

This is the difference between a smart advisor and a capable assistant.

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From APIs to MCP

APIs have been the standard way for software to talk to software. They work well, but they expect a developer to know exactly what endpoint to call and how to call it.

MCP is designed for agents. Instead of fixed, predefined calls, an agent can discover what tools are available from the server and decide which ones to use based on the user’s request.

You could see it as the next step in how we interact with software:

1. GUI: people click buttons.
2. API: programs call endpoints.
3. MCP: agents discover tools and act on them.

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Wrapping Up

MCP provides a simple but important shift:

• LLMs are the reasoning engines.
• Agents wrap LLMs and act as MCP clients.
• MCP servers expose tools and data.
• Systems and APIs provide the actual functionality.
• Data is the final piece the agent needs to access and use.

By introducing MCP, we move agents from just answering questions to actually taking action with real systems and data.

In the next post, we’ll dig into how MCP connects agents to databases, files, and APIs — with practical examples.