Implementing AI Chat Memory with MCP

Implementing AI Chat Memory with MCP
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](/blog/post/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 it
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Introducing CleverChatty – An AI Assistant Package for Go 🤖🐹

Introducing CleverChatty – An AI Assistant Package for Go 🤖🐹
I'm excited to introduce a new package for Go developers: [**CleverChatty**](https://github.com/Gelembjuk/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 us
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Benefits of Using MCP to Implement AI Chat Memory

Benefits of Using MCP to Implement AI Chat Memory
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
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Which MCP Server Transport is Better? Comparing STDIO and SSE

Which MCP Server Transport is Better? Comparing STDIO and SSE
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 serv
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Implementing Authentication in a Remote MCP Server with SSE Transport

Implementing Authentication in a Remote MCP Server with SSE Transport
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
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MCP can have significant impact on habitual internet usage practices

MCP can have significant impact on habitual internet usage practices
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 m
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Building MCP SSE Server to integrate LLM with external tools

Building MCP SSE Server to integrate LLM with external tools
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 a
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