In the case of MCPs, this post is indeed a quick primer. But from a coding standpoint, and despite the marketing that Agent/MCP development simplifies generative LLM workflows, it’s a long coding mess that is hard to tell if it’s even worth it. It’s still the ReAct paradigm at a low level and if you couldn’t find a case for tools then, nothing has changed other than the Agent/MCP hype making things more confusing and giving more ammunition to AI detractors.

This website design is blessed. A great return to the past

Even name takes us back in time - SPARC

If you need to define and write the functions to calculate interest… what exactly is the llm bringing to the table here? I feel like I’m missing something.

The LLM is what decides which endpoint/tool to call (or none at all) in response to the user input.

The original 2022 ReACT paper is still best explainer: https://arxiv.org/abs/2210.03629

maybe some of these could be fit? https://python.langchain.com/docs/tutorials/

MCP is great for when you’re integrating tools locally into IDEs and such. It’s a terrible standard for building more robust applications with multi-user support. Security and authentication are completely lacking.

99% of people wouldn’t be able to find the API keys you need to feed into most MCP servers.

What, according to you, are some alternatives that exist or are in development that fill these gaps?

You are correct ... it is still early days IMHO ... will have to see how this evolves