Introduction to MCP (Model Context Protocol)

If you've been hearing about MCP lately and wondering what all the fuss is about, you're in the right place. The Model Context Protocol (MCP) is a standardized way to connect tools and services to large language models (LLMs) — and understanding it is quickly becoming essential for anyone building AI-powered applications.

How AI API Requests Actually Work

Before diving into MCP itself, it helps to understand what's happening under the hood when you interact with an AI like ChatGPT or Claude. Every conversation you have is actually an API request made up of three components: the conversation structure (system messages and user roles), configuration settings (like temperature and max tokens), and a list of tools the AI can use.

Those tools are what allow an AI to do more than just chat — they let it fetch data from a database, check the weather, or search the internet. Importantly, the AI doesn't execute tools itself. Instead, it responds with the tool it wants to use along with the required arguments in JSON format. Your application then executes the tool, collects the response, and sends it back to the LLM to generate a final answer for the user.

Why Tool Use Alone Isn't Enough

Connecting tools to LLMs isn't a new concept — developers have been doing it for a while. The problem is that every AI provider has structured their tool definitions differently. Google's Gemini uses a function_declarations array with parameters. Anthropic's Claude uses input_schema instead. OpenAI does it differently still. This means that every time you build a tool, you potentially have to rebuild or adapt it for each AI platform you want to support.

That's exactly the gap MCP fills.

What MCP Standardizes

The Model Context Protocol introduces a universal standard so you can build your tools once and have any compatible AI client use them. It's built around two core components:

• MCP Server — This is where your tools and business logic live. For example, if you're building a task management app, your tools for adding or retrieving tasks would be defined here.
• MCP Client — This connects to the MCP server, retrieves the available tools, converts them into the format required by whichever LLM you're using, and presents the results back to the user in a consistent way.

By separating the tool logic from the AI-specific formatting, MCP lets you stop worrying about which provider you're targeting and focus on building great functionality.

Building MCP in Xano

Xano now gives you everything you need to work with MCP directly. You can build an MCP server using your existing business logic, and you also have access to function stack tools for building an MCP client. Whether you're exposing your backend capabilities to AI assistants or orchestrating multi-step AI workflows, Xano's MCP support puts the full protocol within reach — no custom integration work required for each AI provider.