MCP Explained: How Modern AI Agents Connect to the Real World
We saw how the model generates a tool call, our code executes it, and the result gets fed back to the model. This works well enough, but there’s a problem we kind of glossed over. In all of the previously presented examples, we defined the tools ourselves, by hand, in the same Python script as the agent.
- ▪We saw how the model generates a tool call, our code executes it, and the result gets fed back to the model.
- ▪This works well enough, but there’s a problem we kind of glossed over.
- ▪In all of the previously presented examples, we defined the tools ourselves, by hand, in the same Python script as the agent.
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| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/mcp-explained-how-modern-ai-agents-connect-to-the-real-world/ |
| Publication time | Tue, 28 Jul 2026 15:00:00 +0000 |
| Retrieval time | 2026-07-28T15:10:03.980Z |
| Last seen | 2026-07-28T15:10:03.980Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Opening excerpt (first ~120 words) tap to expand
Agentic AI MCP Explained: How Modern AI Agents Connect to the Real World from custom integrations to a universal standard for tool access Maria Mouschoutzi Jul 28, 2026 10 min read Share Image created by the author using ChatGPT Images 2.0 In my latest posts, we’ve talked a lot about tool calling, exploring how AI agents decide which tool to use and how to use it. We saw how the model generates a tool call, our code executes it, and the result gets fed back to the model. This works well enough, but there’s a problem we kind of glossed over. That is, where do the tools come from? In all of the previously presented examples, we defined the tools ourselves, by hand, in the same Python script as the agent.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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