Tag: Mcp

If you’re using tools like Claude Code, GitHub Copilot Workspace, or similar, you’ve probably noticed there’s technical jargon that goes beyond simply “chatting with AI”. I’m talking about terms like rules, commands, skills, MCP, and hooks.

These concepts are the architecture that makes AI agents truly useful for software development. They’re not just fancy marketing words — each one serves a specific function in how the agent works.

Let’s break them down one by one in a clear way.

Lately I’ve been closely following everything around the MCP protocol (Model Context Protocol), and recently I found a project that makes a lot of sense: MCPHero.

The reality is that although MCP is taking off, many “traditional” AI libraries like openai or google-genai still don’t have native MCP support. They only support tool/function calls. MCPHero comes to solve exactly this: make a bridge between MCP servers and these libraries.

What is MCPHero?

MCPHero is a Python library that lets you use MCP servers as tools/functions in native AI libraries. Basically, it lets you connect to any MCP server and use its tools as if they were native OpenAI or Google Gemini tools.

Two protocols, two philosophies

In recent months, two protocols have emerged that will change how we build AI systems: Agent2Agent Protocol (A2A) from Google and Model Context Protocol (MCP) from Anthropic. But here’s the thing: they don’t compete with each other.

In fact, after analyzing both for weeks, I’ve realized that understanding the difference between A2A and MCP is crucial for anyone building AI systems beyond simple chatbots.

The key lies in one question: Are you connecting an AI with tools, or are you coordinating multiple intelligences?

A few months ago, when Anthropic launched their MCP (Model Context Protocol), I knew we’d see interesting integrations between LLMs and databases. What I didn’t expect was to see something as polished and functional as ClickHouse’s AgentHouse so soon.

I’m planning to test this demo soon, but just reading about it, the idea of being able to ask a database questions like “What are the most popular GitHub repositories this month?” and getting not just an answer, but automatic visualizations, seems fascinating.

Yet another protocol promising to change everything

When IBM Research announced the Agent Communication Protocol (ACP) as part of the BeeAI project, my first reaction was the usual one: “Oh, just another universal protocol”. With nearly 30 years in this field, I’ve seen too many “definitive standards” that ended up forgotten.

But there’s something different about ACP that made me pay attention: it doesn’t promise to solve all the world’s problems. It simply focuses on one very specific thing: making AI agents from different frameworks talk to each other. And it does it in a way that really makes sense.

Confession of a converted skeptic

When Anthropic announced the Model Context Protocol (MCP) in November 2024, my first reaction was: “Ah, another protocol promising to solve all integration problems”. As a DevOps Manager who has seen dozens of “universal standards” born and die, I have reasons to be skeptical.

But after several months watching MCP be massively adopted - OpenAI integrated it in March 2025, Google DeepMind in April - I decided to investigate beyond the hype. And I have to admit something: I was wrong.