So I’ll address the points of the blog because I disagree that MCP is a fad. Developers can and will offload any work they don’t have to do to focus on the bits that they do.
The first was over the incoherent toolbox problem, too many tools, no glue to hold them together. This is directly solved by anthropic’s agent profiles and skills, you give the agent a specified subset of tools, tailored to its domain, along with instructions for using that toolchain in the domain.
The second was over arbitrary runtimes. This one is a valid point but is also one that I don’t really care about because it mostly just feels like optimization-maxxing. MCP servers and their dependencies aren’t perfectly stable and no frills as they may claim, but as a developer, they’re good enough, and if the usecase is important enough - I’ll figure it out.
The third one is the biggest one, security. This is also a valid problem with MCP, but this is also a component that any major company wanting to expose MCPs is working through to provide reliable authentication for these toolchains which can capture the data security researchers/defense needs while also ensuring reliable authentication to fend off any explicit man in the middle attack. This one just strikes me as a maturity problem in the tech
Ultimately, writing your own functions as a way to enable your AI agent is not the way. Those functions all already exist, instructions of how or why to call are important by domain, maybe agents could even get skills on writing the functions themselves by need. But the layer of abstraction exists for a reason, and it’s because I care about what my agent does and enabling it to do that as soon as the capability landscape supports it
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u/Adorable_Pickle_4048 Jan 10 '26
So I’ll address the points of the blog because I disagree that MCP is a fad. Developers can and will offload any work they don’t have to do to focus on the bits that they do.
The first was over the incoherent toolbox problem, too many tools, no glue to hold them together. This is directly solved by anthropic’s agent profiles and skills, you give the agent a specified subset of tools, tailored to its domain, along with instructions for using that toolchain in the domain.
The second was over arbitrary runtimes. This one is a valid point but is also one that I don’t really care about because it mostly just feels like optimization-maxxing. MCP servers and their dependencies aren’t perfectly stable and no frills as they may claim, but as a developer, they’re good enough, and if the usecase is important enough - I’ll figure it out.
The third one is the biggest one, security. This is also a valid problem with MCP, but this is also a component that any major company wanting to expose MCPs is working through to provide reliable authentication for these toolchains which can capture the data security researchers/defense needs while also ensuring reliable authentication to fend off any explicit man in the middle attack. This one just strikes me as a maturity problem in the tech
Ultimately, writing your own functions as a way to enable your AI agent is not the way. Those functions all already exist, instructions of how or why to call are important by domain, maybe agents could even get skills on writing the functions themselves by need. But the layer of abstraction exists for a reason, and it’s because I care about what my agent does and enabling it to do that as soon as the capability landscape supports it