Which is not a high bar to clear. It literally only got where it is now because execs and product people love themselves another standard, because if they get their products to support it they can write that on some excel sheet as shipped feature and pin it on their chest. Even if the standard sucks on a technical level and the spec changes all the time.
You need agent to find MCP and what it can be used for (context), similarly you can write what CLI use for e.g. jira.
Rest is up to agent, it needs to list what it can do in MCP, similarly CLI with proper help text will list that.
Regarding context those tools are exactly the same.
When measuring speed running blue team CTFs ("Breaking BOTS" talk at Chaos Congress), I saw about a ~2x difference in speed (~= tokens) for a database usage between curl (~skills) vs mcp (~python). In theory you can rewrite the mcp into the skill as .md/.py, but at that point ... .
Also I think some people are talking past one another in these discussions. The skill format is a folder that supports dropping in code files, so much of what MCP does can be copy-pasted into that. However, many people discussing skills mean markdown-only and letting the LLM do the rest, which would require a fancy bootstrapping period to make as smooth as the code version. I'd agree that skills, when a folder coming with code, does feel like largely obviating MCPs for solo use cases, until you consider remote MCPs & OAuth, which seem unaddressed and core in practice for wider use.
Specifically, MCP is a great unit of encapsulation. I have a secure agent framework (https://github.com/sibyllinesoft/smith-core) where I convert MCPs to microservices via sidecar and plug them into a service mesh, it makes securing agent capabilities really easy by leveraging existing policy and management tools. Then agents can just curl everything in bash rather than needing CLIs for everything. CLIs are still slightly more token efficient but overall the simplicity and the power of the scheme is a huge win.
Now it's completely fine for an AI agent to do the same and blow up their context window.
The single-request-for-all-abilities model + JSON RPC is more token efficient than most alternatives. Less flexible in many ways, but given the current ReAct, etc. model of agentic AI, in which conversations grow geometrically with API responses, token efficiency is very important.
I say this as a hypermedia enthusiast who was hoping to show otherwise.
It could just be fixed to compress the context or the protocol could be tweaked.
Switching to CLIs is like buying a new car because you need an oil change. Sure, in this case, the user doesn’t get to control if the oil change can be done, but the issue is not the car — it’s that no one will do the relatively trivial fix.
For example, I built https://claweb.ai to enable agents to communicate with other agents. They run aw [1], an OSS Go CLI that manages all the details. This means they can have sync chats (not impossible with MCP, but very difficult). It also enables signing messages and (coming soon) e2ee. This would be, as far as I can tell, impossible using MCP.
It breaks most assumptions we have about the shell's security model.
The difference that should be talked about, should be how skills allow much more efficient context management. Skills are frequently connected to CLI usage, but I don't see any reason why. For example, Amp allows skills to attach MCP servers to them – the MCP server is automatically launched when the Agent loads that skill[0]. I belive that both for MCP servers and CLIs, having them in skills is the way for efficent context, and hoping that other agents also adopt this same feature.
That is, I don't think we're gonna be arguing about it for very long.
That's fine if you definition of capabilities is wide enough to include model understanding of the provided tool and token waste in the model trying to understand the tool and token waste in the model doing things ass backwards and inflating the context because it can't see the vastly shorter path to the solution provided by the tool and...
There is plenty of evidence to suggest that performance, success rates, and efficiency, are all impacted quite drastically by the particular combination of tool and model.
This is evidenced by the end of your paragraph in which you admit that you are focused only on a couple (or perhaps a few) models. But even then, throw them a tool they don't understand that has the same capabilities as a tool they do understand and you're going to burn a bunch of tokens watching it try to figure the tool out.
Tooling absolutely matters.
Not only it had lots of issues and security problems all over the place and it was designed to be complicated.
For example, Why does your password manager need an MCP server? [0]
But it still does not mean a CLI is any better for everything.
https://bloomberry.com/blog/we-analyzed-1400-mcp-servers-her...
MCP is far from dead, at least outside of tech circles.
Really it seems to me the difference is that an mcp could be more token-efficient, but it isn't, because you dump every mcp's instructions all the time into your context.
Of course then again skills frequently doesn't get triggered.
just seems like coding agent bugs/choices and protocol design?
It's maybe not optimal to conclude anything from these two. The Vienna school of AI agents focuses on self extending agents and that's not really compatible with MCP. There are lots of other approaches where MCP is very entrenched and probably will stick around.
I have used the kk8s MCP directly inside Github Copilot Chat in VSCode and restricted the write tools in the Configure Tools prompt. With a pseudo protocol established via this MCP and the IDE integration, I find it much safer to prompt the LLM into debugging a live K8s cluster vs. without having any such primitives.
So I don't see why MCPs are or should be dead.
lukol•57m ago