Quick Primer on MCP Using Ollama and LangChain

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.

Is anyone really still using langchain? Has it gotten better? Seemed like a token burning platform the last time I used it.

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.

You know it&#x27;s going to be a great article when the design is from 1995

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

The units for the free memory are interestingly wrong; &#x27;Executing shell command: free -m&#x27; 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!