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.
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.<p>99% of people wouldn’t be able to find the API keys you need to feed into most MCP servers.
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 units for the free memory are interestingly wrong;
'Executing shell command: free -m'
The total system memory is 64222 bytes, with used (available) 8912 bytes.<p>which given that there seems to be no way to specify any data structure or typing in this MCP interface is hardly surprising!