I still prefer MCP over skills

Despite thinking this is AI-generated, I agree but everything has a caveat.

Skills are good for instilling non-repeatable, yet intuitive or institutional knowledge.

MCP’s are great for custom, repeatable tasks. After 5-10 runs of watching my LLM write the same exact script, I just asked it to hardcode the solution and make it a tool. The result is runs are way faster and repeatable.

You could hardcode the script as a file within a skill too right? Skills can contain code, not just markdown files.

For indie developers like myself, I often use chat GPT desktop and Claude desktop for arbitrary tasks, though my main workhorse is a customized coding harness with CC daemons on my nas. With the apps, b I missed having access to my Nas server where my dev environment is. So I wrote a file system MCP and hosted it with a reverse proxy on my Truenas with auth0. I wanted access to it from all platforms CharGPT mobile, desktop. Same for CC.

For chatgpt desktop and Claude desktop my experience with MCPs connected to my home NAS is pretty poor. It(as in the app) often times out fetching data(even though there is no latency for serving the request in the logs), often the existing connection gets invalidated between 2 chat turns and chat gpt just moves on answering without the file in hand.

I am not using it for writing code, its mostly read only access to Fs. Has anyone surmounted these problems for this access patterns and written about how to build mcps to be reliable?

This is the same as saying "I still prefer hammer over screwdriver".

I could not agree any less with the author. I don’t want APIs, I want agents to use the same CLI tooling I already use that is locally available. If my agents are using CLI tooling anyways there is no need to add an extra layer via MCP.

I don’t want remote MCP calls, I don’t even want remote models but that’s cost prohibitive.

If I need to call an API, a skill with existing CLI tooling is more than capable.