What Is MCP? Model Context Protocol

01What Problem Does MCP Solve?

On its own, a language model works only with what it was trained on and the text you paste into the chat. It cannot see your company's order data, calendar, documents or code repository. For it to reach this information, a separate connection has to be built with each tool.

Here is the problem: with five different AI applications and ten different business tools, connecting every pair separately means dozens of custom integrations. Model Context Protocol reduces this tangle to one common language. In a common analogy, MCP is like a USB-C port for AI applications: when the plug is standard, any device can connect to any port.

This standard is also the infrastructure for AI turning from something that only answers into systems that get work done, that is, agents. We explained how agents work in our article what is an AI agent.

02How Does MCP Work?

MCP is based on a client and server architecture. Messaging between the parties uses the JSON-RPC format, and the protocol's official specification is published at modelcontextprotocol.io.

Host and client

The host is the AI application the user interacts with: a desktop chat app, a code editor or a terminal tool. The host creates a client for every server it connects to and manages the model's communication with the servers.

MCP server

An MCP server is a small program that exposes a specific tool or data source through the protocol. A file system server offers folders, a database server offers tables and a CRM server offers customer records to the model. A server can run locally on your own computer or at a remote address.

Connection paths

Local servers usually connect over standard input and output (stdio), while remote servers connect over an HTTP-based transport. For remote servers, authentication and authorization are set up separately. A local server lets files be read without moving them to a separate server; a remote server suits shared use by a team.

03Tools, Resources and Prompts

An MCP server can offer the model three basic building blocks. This distinction makes clear what the model can read and what it can do.

04Claude MCP Example

Because MCP was introduced by Anthropic, the first widespread examples of its use appeared in Claude applications; over time, other AI applications and developer tools began to support the protocol as well. In Claude MCP use, the desktop app starts the servers in the configuration file and offers them to the model.

The example below defines the official file system server so that it can access only a specific folder. For the command to run, the current LTS version of Node.js must be installed on your computer; change the folder path to match your own system.

{
  "mcpServers": {
    "dosyalar": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/kullanici/Belgeler/Raporlar"]
    }
  }
}

claude mcp add dosyalar -- npx -y @modelcontextprotocol/server-filesystem ~/Belgeler/Raporlar
claude mcp list

05MCP Use Cases for Businesses

For AI tool integration, the value of MCP is that it opens scattered company information to an AI assistant in a safe and controlled way. A few typical scenarios:

• Sales and orders: The assistant reads status information from the order system and gives the customer representative a summary.
• Reporting: With a server that reads analytics and advertising data, a manager asks in natural language, "which campaign brought sales last week?"
• Document access: Policies, contracts and technical documents are opened as resources; the assistant bases its answers on them.
• Software development: The code repository, bug logs and test environment are connected through servers; the coding assistant works with more context.
• Hospitality and reservations: A server that reads availability and reservation data lets the in-house assistant give quick answers.

06MCP vs. API vs. Automation

MCP does not replace an API; most MCP servers call an API in the background anyway. The difference is who it is designed for. APIs are designed for developers to use from code, while MCP describes these capabilities in a way that an AI model can understand and choose from: the tool name, its description and its input schema.

The difference from rule-based automation tools lies in the decision mechanism. In tools such as Zapier, Make or n8n the flow is drawn in advance; with a model connected through MCP, the model decides which tool to call and when. For a comparison of the two approaches, see our workflow automation article.

If you are curious how products that put AI at the center from the start are designed, our AI-native application article covers the topic in detail.

07Security: What to Watch For

An MCP server gives AI access to your real systems. This power requires careful security design, especially when using third-party servers.

• Use servers from trusted sources; do not connect a server whose code you have not reviewed to company data.
• Give each server least privilege: limit a file server to a single folder and a database server to a read-only user.
• Ask for user approval for actions such as deleting, payments and external communication.
• Be careful of hidden instructions in external content the model reads (prompt injection); external data should be treated as data, not as instructions.
• Do not leave API keys exposed in configuration files; use environment variables or a secure key store.
• If personal data is processed, assess your KVKK (Turkish data-protection law, comparable to GDPR) obligations; check the current regulations and your legal counsel.

08An Infrastructure Ready for MCP

The precondition for getting real benefit from MCP is that the data to be connected is organized and accessible. For a business whose orders, stock or reservations sit in hand-kept spreadsheets, that data first has to be brought together in a single system.

An e-commerce integration where marketplace, shipping and e-invoice (Turkish e-invoicing) connections run from one panel, hotel software that collects reservation and guest information, or industry software solutions built for your business create a clean data source that can be opened to AI with an MCP server tomorrow. You can also look at our integrated solutions service for joining different systems into a consistent structure.

The picture is similar on the marketing side: when advertising, analytics and CRM data are kept in order, AI assistants make campaign performance queryable in natural language. Setting up this measurement framework is part of digital marketing work.