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MCP Servers What They Are and How to Use Them

September 10, 2025

In the evolving world of AI and software development, you may have started hearing about MCP servers. If you’re curious what they are and how you can actually use them, this post will walk you through the basics.

What Is an MCP Server?

MCP stands for Model Context Protocol. It’s a protocol designed to help AI models (like GPT) connect to external tools, data sources, and services in a structured, safe, and standardized way.

Think of an MCP server as a “bridge” between an AI assistant and the outside world. Instead of giving an AI unrestricted access to your computer or APIs, you set up an MCP server that:

  • Exposes specific tools or resources the AI can use
  • Enforces permissions and boundaries
  • Provides structured data in a consistent format

This makes it easier and safer to let AI systems interact with your files, databases, APIs, or custom workflows.

Why Use MCP Servers?

MCP servers bring some key advantages:

  1. Safety & Security – Instead of giving raw API keys or file access, you expose only the functions you want.
  2. Consistency – All servers follow the same protocol, so once you know how to use one, you can use others.
  3. Extensibility – You can build your own MCP server for any system (e.g., GitHub repos, databases, internal company tools).
  4. Separation of Concerns – AI doesn’t need to “know” how your tool works internally—it just asks the MCP server.

How MCP Servers Work

At a high level, the setup looks like this:

AI Assistant (Client)  <----->  MCP Server  <----->  External Resource
  • The client (usually an AI assistant or IDE plugin) speaks the MCP protocol.
  • The MCP server exposes a set of capabilities: listing resources, performing actions, streaming results.
  • The external resource can be anything—like a database, file system, or API.

The client doesn’t talk directly to the resource; it only communicates through the server.

How to Use an MCP Server

Here’s a simple workflow:

  1. Choose or build an MCP server

    • There are already some open-source MCP servers (for example, ones that let GPT interact with local files or Git).
    • You can also create your own by following the MCP spec.

  2. Connect it to your AI assistant

    • Most assistants that support MCP will have a configuration file where you can list available servers.
    • Each server runs as a standalone process that the assistant can talk to.

  3. Start using tools through the AI

    • Once connected, you can ask the assistant to perform actions (like “search my repo for a function” or “query my database”), and it will do so through the MCP server.