# CleverChatty now supports HTTP streaming for MCP servers!
**CleverChatty**, a lightweight AI Chat tool supporting multiple LLM providers, now includes support for **HTTP streaming** with MCP servers. This update enables more flexible and efficient communication with AI models, making integration with custom tools and services even smoother.
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## 🌐 What is CleverChatty?
[CleverChatty](https://github.com/Gelembjuk/cleverchatty) is a minimalist AI chat interface that works with various large language model (LLM) providers — including OpenAI, Anthropic, Google, and local models
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mcp, ai-orchestrator, ai-assistant, rag, ai-memory, ai-agents
Over the past couple of months, I’ve been experimenting with the Model Context Protocol (MCP) — building AI agents and tools around it. While the experience has been promising, I’ve noticed a few areas where MCP could be expanded or improved.
These aren’t critical issues, but adding them would make MCP more complete and developer-friendly.
Here’s my current wishlist:
1. **A Standard MCP Server Interface**
2. **Bidirectional Notifications**
3. **Built-in or Native Transport Layer**
Let’s walk through each of these in more detail.
## 1. A Standard MCP Server Interface
Several MC
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RAG, MCP, AI Orchestrator, CleverChatty, LLM, AI Assistant
Good news! I've extended my lightweight AI orchestrator, **CleverChatty**, to support Retrieval-Augmented Generation (RAG) by integrating it using the **Model Context Protocol (MCP)**.
### Quick Recap
* **RAG (Retrieval-Augmented Generation)** is an AI technique that enhances language models by retrieving relevant external documents (e.g., from databases or vector stores) based on a user’s query. These documents are then used as additional context during response generation, enabling more accurate, up-to-date, and grounded outputs.
* **MCP (Model Context Protocol)** is a standard
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LLM, контекст, ШІ, машинне навчання
Великі мовні моделі (LLM), такі як GPT-4, Claude, Mistral та інші, здаються розумними у своїх відповідях — але справжня магія полягає в тому, як вони сприймають і інтерпретують контекст. Розуміння того, що входить у контекст LLM і як це впливає на результат, критично важливе для розробників, дослідників і дизайнерів продуктів, які працюють із генеративним ШІ.
У цій публікації я хочу дослідити складові контексту, його структуру, обмеження та взаємодію з найбільш поширеними сценаріями використання, такими як використання інструментів (Tools, MCP) і включення додаткових знань з Retrie
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MCP, server, AI, machine learning
It seems the MCP hype is starting to slow down a bit. After 6–8 months of high enthusiasm, the community is beginning to realize that MCP is not a magic bullet. In some MCP listings, you’ll find more than 10,000 servers doing all sorts of things. Naturally, many of them are useless—spun up by enthusiasts just to see what MCP is all about.
But some of these servers are actually useful.
In this post, I want to share my thoughts on building the most universal MCP server—one that can adapt to almost any use case.
## Quick Recap: What Is MCP?
MCP stands for **Model Context Protocol**
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AI, agents, planning, autonomy, MCP
I continue to explore one of my favorite topics: how to make AI agents more independent. This blog is my way of organizing ideas and gradually shaping a clear vision of what this might look like in practice.
### The Dream That Started It All
When large language models (LLMs) and AI chat tools first started delivering truly impressive results, it felt like we were entering a new era of automation. Back then, I believed it wouldn’t be long before we could hand off any intellectual task to an AI—from a single prompt.
I imagined saying something like:
> "Translate this 500-page nove
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LLM, context, AI, machine learning
Large Language Models (LLMs) like GPT-4, Claude, and Mistral appear to produce intelligent responses — but the magic lies in how they consume and interpret *context*. Understanding what goes into an LLM's context and how it shapes output is critical for developers, researchers, and product designers working with generative AI.
This post explores the components of context, how it's structured, how it's limited, and how advanced use cases like tool usage and retrieval-augmented generation (RAG) interact with it.
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## What Is Context in an LLM?
"Context" refers to the entire inpu
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transport, MCP, servers, programming
I would like to expose one more benefit of the Model Context Protocol (MCP) — the ability to easily change the transport protocol. There are three different transport protocols available now, and each has its own benefits and drawbacks.
However, if an MCP server is implemented properly using a good SDK, then switching to another transport protocol is easy.
## 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
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In recent months, the Model Context Protocol (MCP) has gained a lot of traction as a powerful foundation for building AI assistants. While many developers are familiar with its core request-response flow, there's one feature that I believe remains underappreciated: the ability of MCP servers to send **notifications to clients**.
Let’s quickly recap the typical flow used by most MCP-based assistants:
* A user sends a prompt to the assistant.
* The assistant attaches a list of available tools and forwards the prompt to the LLM.
* The LLM generates a response, possibly requesting the
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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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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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memory, AI, conversational agents
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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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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mcp, llm, authentication, sse, server-sent-events, golang, python
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, ai, llm, chatgpt, internet, web3, sse, server-sent events
I am interested in learning how LLMs can understand requests requiring a "tool call".
In this post ["Tool Calling" and Ollama](https://k33g.hashnode.dev/tool-calling-and-ollama), there is a nice description of how "Tool calling" works with Ollama.
The idea of this feature is that LLMs can have access to some tools (aka external APIs) and can call them to get extra information. To be able to do this, the LLM has to understand the current request, determine that this request could be forwarded to a tool, and parse the arguments.
Here is a shorter example of the code from the origin
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mcp, ai, llm, chatgpt, internet, web3, sse, server-sent events
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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mcp, sse, llm, external-tools, linux, security, authorization-token
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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