Recently, I introduced the idea of using MCP (Model Context Protocol) to implement memory for AI chats and assistants. The core concept is to separate the assistant's memory from its core logic, turning it into a dedicated MCP server.

If you're unfamiliar with this approach, I suggest reading my earlier article: Benefits of Using MCP to Implement AI Chat Memory.

What Do I Mean by “AI Chat”?

In this context, an "AI Chat" refers to an AI assistant that uses a chat interface, with an LLM (Large Language Model) as its core, and supports calling external tools via MCP. ChatGPT is a good example.

Throughout this article, I’ll use the terms AI Chat and AI Assistant interchangeably.

I'm excited to introduce a new package for Go developers: CleverChatty.
CleverChatty implements the core functionality of an AI chat system. It encapsulates the essential business logic required for building AI-powered assistants or chatbots — all while remaining independent of any specific user interface (UI).

In short, CleverChatty is a fully working AI chat backend — just without a graphical UI. It supports many popular LLM providers, including OpenAI, Claude, Ollama, and others. It also integrates with external tools using the Model Context Protocol (MCP).

Implementing memory for AI assistants or conversational AI tools remains a complex engineering challenge. Large Language Models (LLMs) like ChatGPT are stateless by design—they only retain knowledge up to their training cutoff and do not inherently remember past interactions. However, for a seamless and context-aware user experience, it’s crucial for AI chat tools to recall previous conversations, preferences, and relevant history.

To address this gap, different vendors have developed their own proprietary solutions for integrating memory. For example, OpenAI’s ChatGPT has built-in memory capabilities, and other platforms like Anthropic’s Claude (including the Claude Desktop application) offer similar features. Each of these implementations is unique, often tied closely to the platform’s internal architecture and APIs.

This fragmented landscape raises an important question: what if we had a standardized way to implement memory for AI assistants?

Model Context Protocol (MCP) was originally designed to provide a standard way to integrate external tools with large language models (LLMs). But this same concept could inspire a standardized approach to implementing memory in AI chat systems. Instead of inventing something entirely new, perhaps we can extend or repurpose MCP to serve this function as well.

In this post, I’d like to share some thoughts on the Model Context Protocol (MCP) and compare two types of server integration methods it supports—STDIO and SSE, especially from the security perspective.

Quick Recap: What is MCP?

• Model Context Protocol (MCP) is a new standard for integrating external tools with AI chat applications. For example, you can add Google Search as an MCP server to Claude Desktop, allowing the LLM to perform live searches to improve its responses. In this case, Claude Desktop is the MCP Host.

There are two common types of MCP server transports:

• STDIO Transport: The MCP server runs locally on the same machine as the MCP Host. Users download a small application (the MCP server), install it, and configure the MCP Host to communicate with it via standard input/output.

• SSE Transport: The MCP server runs as a network service, typically on a remote server (but it can also be on localhost). It's essentially a special kind of website that the MCP Host connects to via Server-Sent Events (SSE).

Today, I want to show how Model Context Protocol (MCP) servers using SSE transport can be made secure by adding authentication.

I'll use the Authorization HTTP header to read a Bearer token. Generating the token itself is out of scope for this post, it is same as usual practices for web applications.

To verify how this works, you’ll need an MCP host tool that supports SSE endpoints along with custom headers. Unfortunately, I couldn’t find any AI chat tools that currently support this. For example, Claude Desktop doesn’t, and I haven’t come across any others that do.

However, I’m hopeful that most AI chat tools will start supporting it soon — there’s really no reason not to. By the way, I shared my thoughts on how MCP could transform the web in this post.

For my experiments, I’ve modified the mcphost tool. I’ve submitted a pull request with my changes and hope it gets accepted. For now, I’m using a local modified version. I won’t go into the details here, since the focus is on MCP servers, not clients.

Model Context Protocol (MCP) is now popular subject in discussions around AI and LLMs. It was designed to add a standard way to connect "external" tools to LLMs to make them more useful. Classic example is the "what is the weather in ..." too. Each AI chat tool could do this with own way. Now there is a standard and a plugin made for one Ai Chat system can work with others.

We can se burst of enthusiasm in implementig of MCP servers for everything. I expect this trend will grow. Especially usage of MCP servers with SSE transport. Implementing of MCP server with Server-Sent Events make it similar to SaaS server designed for LLM/AI tool as a client.

There are to reason i decided to write this artcile.

• First. It is reported that internet users now often go to AI chat (often ChatGPT) to find something instead of going to google
• Second. OpenAI anounced they will add support of MCP to ChatGPT Desktop soon. And they will add both STDIO and SSE transport protocols for MCP

Based on this i expect we will see some interesting changes soon.

As large language models (LLMs) find real-world use, the need for flexible ways to connect them with external tools is growing. The Model Context Protocol (MCP) is an emerging standard for structured tool integration.

Most current tutorials focus on STDIO-based MCP servers (Standard Input/Output), which must run locally with the client. But MCP also supports SSE (Server-Sent Events), allowing remote, asynchronous communication over HTTP—ideal for scalable, distributed setups.

In this article, we'll show how to build an SSE-based MCP server to enable real-time interaction between an LLM and external tools.

For this example, I've chosen the "Execute any command on my Linux" tool as the backend for the MCP server. Once connected to an LLM, this setup enables the AI to interact with and manage a Linux instance directly.

Additionally, I'll demonstrate how to add a basic security layer by introducing authorization token support for interacting with the MCP server.