Tools: Code Is All You Need

Summary

Tools: Code Is All You Need written on July 03, 2025 If you've been following me on Twitter, you know I'm not a big fan of MCP right now. It's not that I dislike the idea; I just haven't found it to work as advertised. In my view, MCP suffers from two major flaws: It isn’t truly composable. Most composition happens through inference. It demands too much context. You must supply significant upfront input, and every tool invocation consumes even more context than simply writing and running code. A quick experiment makes this clear: 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. But MCP is the Future! I want to address some of the feedback I've received on my stance on this. I evaluated MCP extensively in the context of agentic coding, where its limitations were easiest to observe. One piece of feedback is that MCP might not make a ton of sense for general code generation, because models are already very good at that but they make a lot of sense for end-user applications, like, say, automating a domain-specific task in a financial company. Another one is that I need to look at the world of the future, where models will be able to reach many more tools and handle much more complex tasks. My current take is that my data indicates that current MCP will always be harder to use than writing code, primarily due to the reliance on inference. If you look at the approaches today for pushing towards higher tool counts, the proposals all include a layer of filtering. You pass all your tools to an LLM and ask it to filter it down based on the taks at hand. So far, there hasn't been much better approaches proposed. The main reason I believe this will most likely also hold true — that you shouldn't be using MCP in its current form even for non-programming, domain-specific tasks — is that even in those cases code generation ju...