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I still prefer MCP over skills
111–120 of 415 posts
Re: I still prefer MCP over skills
#112I 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.
Cool cool. Except. What about auth? Authn and authz. Agent should be you always? If not, every API supports keys? If so, no fears about context poisoned agents leaking those keys? One thing an MCP (server) gives you is a middleware layer to control agent access. Whether you need that is use-case dependent.
> If not, every API supports keys?
How would MCP help you if the API does not support keys?
But that's not the point. The agent calls CLI tools, which reads secrets from somewhere where the agent cannot even access. How can agent leak the keys it does not have access to?
You ARE running your agents in containers, right?
Re: I still prefer MCP over skills
#113Earlier quoted context omitted.
Why are they not calling APIs directly with strictly defined inputs and outputs like every other internal application? The story for MCP just makes no sense, especially in an enterprise.
MCP really only makes sense for chatbots that don’t want to have per session runtime environments. In that context, MCP makes perfect sense. It’s just an adapter between an LLM and an API. If you have access to an execution engine, then yes CLI + skills is superior.
Only is doing a lot of work here. There are tons of use cases aside from local coding assistants, e.g., non-code related domain specific agentic systems; these don’t even necessarily have to be chatbots.
Re: I still prefer MCP over skills
#114This argument always sounds like two crowds shouting past each other.
Are you a solo developer, are you fully in control of your environment, are you focused on productivity and extremely tight feedback loops, do you have a high tolerance for risk: you should probably use CLIs. MCPs will just irritate you.
Are you trying to work together with multiple people at organizational scale and alignment is a problem; are you working in a range of environments which need controls and management, do you have a more defensive risk tolerance ... then by the time you wrap CLIs into a form that are suitable you will have reinvented a version of the MCP protocol. You might as well just use MCP in the first place.
Aside - yes, MCP in its current iteration is fairly greedy in its context usage, but that's very obviously going to be fixed with various progressive-disclosure approaches as the spec develops.
Re: I still prefer MCP over skills
#115> Using a skill often requires loading the entire SKILL.md into the LLM’s context window, rather than just exposing the single tool signature it needs.
Isn't this, like, the exact thing MCP is the worst at? You need to load the entire MCP into the context even if you're not using the MCP's relevant functions. Which is why some people put them on subagents, which is like, equivalent to putting the MCP behind a CLI function, at which point, why not just have the CLI function and selectively load it when yo- OH WAIT, THERE'S A NAME FOR THAT!
Re: I still prefer MCP over skills
#116Earlier quoted context omitted.
What are the advantages of using an environment that doesn't have access to a CLI, only having to run/maintain your own server, or pay someone else to maintain that server, so AI has access to tools? Can't you just use AI in the said server?
Obvious example is a corporate chatbot (if it's using tools, probably for internal use). Non-technical users might be accessing it from a phone or locked-down corporate device, and you probably don't want to run a CLI in a sandbox somewhere for every session, so you'd like the LLM to interface with some kind of API instead. Although, I think MCP is not really appropriate for this either. (And frankly I don't think ch…
> and you probably don't want to run a CLI in a sandbox somewhere for every session
You absolutely DO want to run everything related to LLMs in a sandbox, that's basic hygiene
Re: I still prefer MCP over skills
#117This argument always sounds like two crowds shouting past each other. Are you a solo developer, are you fully in control of your environment, are you focused on productivity and extremely tight feedback loops, do you have a high tolerance for risk: you should probably use CLIs. MCPs will just irritate you. Are you trying to work together with multiple people at organizational scale and alignment is a problem; are you…
Context usage is a client problem - progressive disclosure can be implemented without any spec changes (Claude/code has this built in for example). That being said the examples for creating a client could be massively expanded to show how to do this well
Re: I still prefer MCP over skills
#118Earlier quoted context omitted.
MCP really only makes sense for chatbots that don’t want to have per session runtime environments. In that context, MCP makes perfect sense. It’s just an adapter between an LLM and an API. If you have access to an execution engine, then yes CLI + skills is superior.
Only is doing a lot of work here. There are tons of use cases aside from local coding assistants, e.g., non-code related domain specific agentic systems; these don’t even necessarily have to be chatbots.
OP's point is about per session sandboxes, not them necessarily being "chatbots". But if you don't burry the agent into a fresh sandbox for every session you have bigger problems to worry about than MCP vs CLI anyway
Re: I still prefer MCP over skills
#119[dead]
Re: I still prefer MCP over skills
#120Despite 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.
It is all about API contracts, right?
After the first run, you have a script and an API: the agent discovery mechanism is a detail. If the script is small enough, and the task custom enough, you could simply add the script to the context and say "use this, adapt if needed".
Or am I misunderstanding you?