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what does "MCP" stand for?
Oh wait... hm ;) perhaps the writing nerds had it right when they recommend always writing the full acronym out the first time it's used in an article, no matter how common one presumes it to be
what does "MCP" stand for?
I was about to say the same thing. It’s bad writing practice to do this, even if you are assuming your followers are following you. Especially for a site like Twitter that has a login wall.
> This is a significant advantage that an MCP (Multi-Component Pipeline) typically cannot offer Oh god please no, we must stop this initialism. We've gone too far.
It's the wrong acronym. I wrote this blog post on the bike and used an LLM to fix up the dictation that I did. While I did edit it heavily and rewrote a lot of things, I did not end up noticing that my LLM expanded MCP incorrectly. It's Model Context Protocol.
what does "MCP" stand for?
The job then becomes identifying those problems and figuring out how to configure a sandbox for them, what tools to provide and how to define the success criteria for the model.
That still takes significant skill and experience, but it's at a higher level than chewing through that problem using trial and error by hand.
My assembly Mandelbrot experiment was the thing that made this click for me: https://simonwillison.net/2025/Jul/2/mandelbrot-in-x86-assem...
Something I've realized about LLM tool use is that it means that if you can reduce a problem to something that can be solved by an LLM in a sandbox using tools in a loop, you can brute force that problem. The job then becomes identifying those problems and figuring out how to configure a sandbox for them, what tools to provide and how to define the success criteria for the model. That still takes significant skill an…
I treat an LLM the same way I'd treat myself as it relates to context and goals when working with code.
"If I need to do __________ what do I need to know/see?"
I find that traditional tools, as per the OP, have become ever more powerful and useful in the age of LLMs (especially grep).
Furthermore, LLMs are quite good at working with shell tools and functionalities (heredoc, grep, sed, etc.).
> try completing a GitHub task with the GitHub MCP, then repeat it with the gh CLI tool. You'll almost certainly find the latter uses context far more efficiently and you get to your intended results quicker. This is spot on. I have a "devops" folder with a CLAUDE.md with bash commands for common tasks (e.g. find prod / staging logs with this integration ID). When I complete a novel task (e.g. count all the rows that…
Directionally I think this is right. Most LLM usage at scale tends to be filling the gaps between two hardened interfaces. The reliability comes not from the LLM inference and generation but the interfaces themselves only allowing certain configuration to work with them. LLM output is often coerced back into something more deterministic such as types, or DB primary keys. The value of the LLM is determined by how well…
Consider internal company tools or niche APIs with minimal online documentation. Sure, you could dump all the documentation into context for code generation, but that often requires more context than interacting with an MCP tool. More importantly, generated code for unfamiliar APIs is prone to errors so you'd need robust testing and retry mechanisms built in to the process.
With MCP, if the tools are properly designed and receive correct inputs, they work reliably. The LLM doesn't need to figure out API intricacies, authentication flows, or handle edge cases - that's already handled by the MCP server.
So I agree MCP for GitHub is probably overkill but there are many legitimate use cases where pre-built MCP tools make more sense than asking an LLM to reverse-engineer poorly documented or proprietary systems from scratch.