1. Ask the LLM to build a tool, under your guide and specification, in order do a specific task. For instance, if you are working with embedded systems, build some monitoring interface that allows, with a simple CLI, to do the debugging of the app as it is working, breakpoints, to spawn the emulator, to restart the program from scratch in a second by re-uploading the live image and resetting the microcontroller. This is just an example, I bet you got what I mean.

2. Then write a skill file where the usage of the tool at "1" is explained.

Of course, for simple tasks, you don't need the first step at all. For instance it does not make sense to have an MCP to use git. The agent knows how to use git: git is comfortable for you, to use manually. It is, likewise, good for the LLM. Similarly if you always estimante the price of running something with AWS, instead of an MCP with services discovery and pricing that needs to be queried in JSON (would you ever use something like that?) write a simple .md file (using the LLM itself) with the prices of the things you use most commonly. This is what you would love to have. And, this is what the LLM wants. For complicated problems, instead, build the dream tool you would build for yourself, then document it in a .md file.

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.

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.

That being said, majority of users on this planet don't use AI agents like that. They go to ChatGPT or equivalent. MCP in this case is the obvious choice because it provides remote access and it has better authentication story.

In order to make any argument about pro/con of MCP vs Skills you first need to find out who is the user.

A skill is just a description for how to use an existing CLI tool. You don't need to write new code for the LLM to interact with some system. You just tell the LLM to use the same tool humans do. And if you find the CLI is lacking in some way, you can improve it and direct human usage benefits from that improvement too.

On the other hand, an MCP requires implementing a new API for a service, an API exclusive to LLMs, and keeping parallel documentation for that. Every hour of effort put into it is an hour that's taken away from improving the human-facing API and documentation.

The way skills are lazy-loaded when needed also keeps context clean when they're not used. To be fair, MCPs could be lazy-loaded the same way, that's just an implementation detail.

MCP makes a lot of sense for enterprise IMO. Defines auth and interfaces in a way that's a natural extension of APIs.

Despite many decades of proof that automation simplifies and reveals the illogical in organisations, digitisation has mostly stopped at below the “CXO” level - and so there are not APIs or CLIs available to anyone - but MCP is cutting through

Just consider:

Throughout companies large and small, Agile is what coders do, real project managers still use deadlines and upfront design of what will be in the deadline - so any attempt to convert the whole company to react to the reality of the road is blocked

Reports flow upwards - but through the reporting chain. So those PowerPoints are … massaged to meet to correct story, and the more levels it’s massaged the more it fails to resemble reality. Everyone knows this but managing the transition means potentially losing control …

There are plenty of digitisationmprojects going on - but do they enable full automation or are they another case of an existing political arena building its own political choices in software - “our area in a database to be accessed via an UI by our people” - almost never “our area to be used by others via API and totally replacing our people”.

(I think I need to be more persuasive

In my case, my MCP is setup with the endpoints being very thin LLM facing layer with the meat of the action being done by helper methods. I also have cli scripts that import/use the same helpers so the core logic is centralized and the only difference is that thin layer, which could be the LLM endpoint or cli's argparse. If I need another type of interface, that can also call the same helpers.

MCP has severe context bloat just by starting a thread. If harnesses were smart enough to, during install time, summarize the tools provided by a MCP server (rather than dumping the whole thing in context), it would be better. But a worse problem is that the output of MCP goes straight into the context of the agent, rather than being piped somewhere else

A solution is to have the agent run a cli tool to access mcp services. That way the agent can filter the output with jq, store it in a file for analysis later, etc

So it's really all about availability or preference. Personally, I don't think we needed a whole new standard with all its complexities and inevitable future breaking changes etc.

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 seems like a lot of the discussion is arguing in favor of API usage without realizing that MCP basically standardizes a universal API, thus enabling code mode.

A simplified but clarifying way to think about it is that MCP exposes all the things that can be done, and Skills encode a workflow/expertise/perspective on how something should be done given all the capabilities.

So I'm not sure why the article portrays one to be conflicting with the other (e.g. "the narrative that “MCP is dead” and “Skills are the new standard” has been hammered into my brain. Everywhere I look, someone is celebrating the death of the Model Context Protocol in favor of dropping a SKILL.md into their repository.").

You can just not choose to use a skill if it's not useful. But if it's useful a skill can add to what an MCP alone can do.

This allows the non-engineers (and also engineers) to use Claude Desktop to do day-to-day operations (e.g. ban user X for fraud) and analytics (e.g. how much revenue we made past 7 days? Any fraud patterns?). The MCP helps to add audit, authorization, and approval layer (certain ops action like banning user will require approval).

This only works for people using agents themselves on computers they control, rather than, e.g., the Claude web app, but is a good chunk of my usage.

I think people are either over or under thinking the auth piece, though. The agent should have access to their own token. Both CLIs and MCPs and even raw API requests work this way. I don't think MCPs provide any further security. You should assume the agent can access anything in its environment and do everything up to what the credential permits. You don't want to give your more powerful credential to the MCP server and hope that the MCP server somehow restricts the agent to doing less (it can probably find the credential and make out-of-band calls if it wants). The only way I think it could work like that is how... is it Sprite does it?... where you give use a fake token and have an off-machine proxy that it goes through where it MitMs the request and injects the real credential.

Take Codex, for example, it does not support the MCP prompts spec[0][1] which is quite powerful because it solves a lot of friction with deploying and synchronizing SKILL.md files. It also allows customization of virtual SKILL.md files since it allows compositing the markdown on the server.

It baffles me why such a simple protocol and powerful capability is not supported by Codex. If anyone from OpenAI is reading this, would love to understand the reasoning for the poor support for this relatively simple protocol.

[0] https://github.com/openai/codex/issues/5059

[1] https://modelcontextprotocol.io/specification/2025-06-18/ser...

Than pass the program, your server or application can parse the instructions and work from the generated AST to do all sorts of interesting things, within the confines of your language features.

It's verifiable, since you are providing within the defined grammar, and with the parser provided.

It is implicitly sandboxed by the powers you give (or rather exclude) to your runtime via an interpreter/compiler

I've tried this before for a grammar I defined for searching documents, and found it to be quite good at creating valid often complex search instructions.

Wrong. It needs to "understand" both these things. The only difference is where and how the strings explaining them are generated.

If you're using an agent in a shell environment with unfettered internet access and code execution: CLI + Skills.

If you're using a hosted agent on a website or in an app without code execution and limited/no internet access: MCP.

We want both patterns. Folks who are agro about MCP do ~all of their work in the former, so it seems pointless. Most people interact with agents in the later.

Also, with skills, you can organize your files in a hierarchy with the parent page providing the most general overview and each child page providing a detailed explanation of each endpoint or component with all possible parameters and errors. I also made a separate page where I list all the common issues for troubleshooting. It works very well.

I created some skills for my no-code platform so that Claude could access and make changes to the control panel via HTTP. My control panel was already designed to update in real-time so it's cool to watch it update as Claude creates the schema and adds dummy data in the background.

I spent a huge amount of effort on refining my HTTP API to make it as LLM-friendly as possible with flexible access control.

You can see how I built my skills marketplace from the docs page if anyone is interested: https://saasufy.com/

Everything will go to the simplest and most convenient, often both, despite the resistance of the complexity lovers.

Sorry MCP, you are not as simple as CLI/skill/combination, and no, you are not more secure just because you are buried under 3 level of spaghetti. There are no reason for you to exist, just like Copilot. I don't just wish, but know you'll go into obscurity like IE6.

Both are useful to different people (and role families) in different ways and if you don't feel certain pain points, you may not care about some of the value they provide.

Agent skills are useful because they're standardized prompt sharing but more than that, because they have progressive disclosure so you don't bloat your context with an inefficietly designed MCP and their UX is very well aligned such that "/SkillBuilder" skills are provided from the start and provide a good path for developers or non traditional builders to turn conversations into semi or full automation. I use this mental model to focus on the iteration pattern and incremental building [1].

[1] https://alexhans.github.io/posts/series/evals/building-agent...

Skills would have required me to 1) add all the skill files to all those projects (and maintain all those files), and 2) install software tools (some of these tools don't have CLIs) to be usable by the skills. Not to mention: the skills aren't deterministic! You have to iterate on a skill file for a while to get the LLM to reliably use it the way you want.

If all you need is "teach the model how to use an existing tool", then use a skill, or even scripts, which are great for bulk work or teaching workflows.

MCPs are good at giving agents a stable, app-owned interface to a system w/o making the agents having to rediscover the integration every session. There's no way a skill/script would be able to handle the stuff I do via my local MCPs for managing certain apps and databases.

I’ll often see the agent saying it’s about to do something so I’ll stop it and ask “what does the xxx skill say about doing that?’ And it’ll go away and think and then say “oh, the skill says I should never do that”

For chatgpt desktop and Claude desktop my experience with MCPs connected to my home NAS is pretty poor. It(as in the app) often times out fetching data(even though there is no latency for serving the request in the logs), often the existing connection gets invalidated between 2 chat turns and chat gpt just moves on answering without the file in hand.

I am not using it for writing code, its mostly read only access to Fs. Has anyone surmounted these problems for this access patterns and written about how to build mcps to be reliable?

    > ChatGPT can’t run CLIs. Neither can Perplexity or the standard web version of Claude. Unless you are using a full-blown compute environment (like Perplexity Computer, Claude Cowork, Claude Code, or Codex), any skill that relies on a CLI is dead on arrival. 

I can see Skills becoming a standard soon. But the concern still holds. When you publish a MCP you liberate the user out of installing anything. But with skills what happens if the skill running environment don't have access to the cli binary or if it isn't in PATH?

The same thing plays out at the language layer. The pain of C++ multiple inheritance drove people toward better abstractions. If LLM's absorb that friction before it reaches anyone, the signal that produces the next Go never gets felt by the people who could act on it.

Wrote about where that leads: https://blog.covet.digital/a/the_last_language_you_can_read....

I think this is underappreciated. CLI access gives agents a ton of freedom and might be more effective in many applications. But if you require really fine granularity on permissions -- e.g., do lookups in this db and nothing else -- MCP is a natural fit.

That is a meaningful distribution shift. Products no longer need to be marketed to end users if an agent can find and invoke them directly. Skills require the developer to install them ahead of time, which means someone already decided this tool was relevant.

E.g. if I have some ElasticSearch cluster, I use a skill to describe the data, and if I ask the LLM to write code that queries ElasticSearch but to test it first it can use a combination of skill + MCP to actually run a query.

I think this model works nicely.

This is how I am structuring stuff in Claude Code

- Ansible setup github cli, git, atlassian cli, aws-cli, terraform cli tooling

- Claude hooks for checking these cli tools are authenticated and configured

- Claude skills to use the CLI tooling

No, a CLI with RPC can do exactly that, just smaller. It goes lower in the exact same stack without the fluff.

Imagine you are creating an asset which requires multiple API calls and your UI is designed to go through a 10-12 step setup process for that asset. In practice even if we give one tool for LLM to one-shot it, or even if we break it down into 10-12 tools the points of hallucinations are much higher.

Contrast this with "skills" and CLI.

I started out building an MCP server for an internal wiki, but ended up replacing it with a simple CLI + skill because the wiki had no access control and the simpler setup was good enough in practice.

I think that's the important boundary, though: once access control, auth, or per-user permissions enter the picture, I'd much rather have MCP as the interface than rely on local tooling conventions.

You may dislike MCP, and there are certainly valid arguments to be made there, but that doesn't mean you can replace it with skills. If you could replace a given MCP server with a skill it would only indicate that someone misunderstood the assignment and chose the wrong tool in the first place. It wouldn't indicate the superiority of one thing over the other.

This whole article, and it's current rank on HN (#5), is making me feel like I took crazy pills this morning. A colleague suggests this Skills vs MCP discourse is big on Twitter, so maybe I lack the necessary background to appreciate this, but aren't these different tools, solving for different things, in different ways? Is this parody? Am I falling into a bot engagement trap by even responding to this? The article certainly reads like LinkedIn drivel, with vague, emphatic opinions about nothing.

That's exactly the problem. As agents become better and can read API documentation themselves, WHY do you need an API abstraction?

The first is using agents locally to develop.

The second is developing an agent. Not necessarily for coding, mind you. Not even for just text sometimes.

They are different cases, MCP is great for the latter.

I built this to solve this exact problem. https://github.com/turlockmike/murl

- "CLIs need to be published, managed, and installed" -- same for MCP servers which you have to define in your config, and they frequently use some kind of "npx mcp-whatever" call.

- "Where do you put the API tokens required to authenticate?" -- where does an MCP server put them? In your home folder? Some .env file? The keychain? Same like CLI tools.

- "Some tools support installing skills via npx skills, but that only works in Codex and Claude Code, not Claude Cowork or standard Claude" -- sure, but you also can't universally define MCP servers for all those tools. You have to go ahead and edit the config anyway.

- "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" -- yeah, but it's on-demand rather than exposing ALL MCP servers' tool signatures. Have you ever tried to use playwright MCP?

I just don't buy the "without any setup" argument.

But what really changed my mind is seeing how much more casual scripting the LLMs do these days. They'll build rad unix pipes, or some python or node short scripts. With CLI tools, it all composes: every trick it learns can plug directly into every other capability.

Where-as with MCP, the LLM has to act as the pipe. Tool calls don't compose! It can read something like this tmux skill then just adapt it in all sorts of crazy ways! It can sort of do that with tool calls, but much less so. https://github.com/nickgnd/tmux-mcp

I'd love to see a capnproto capnweb or some such, with third party handoff (apologies Kenton for once again raising 3ph), where a tool call could return a result and we could forward the result to a different LLM, without even waiting for the result to come back. If the LLM could compose tool calls, it would start to have some parity with the composability of the cli+skill. But it doesn't. And as of very recently I've decided that is too strong a selling point to be ignored. I also just like how the cli remains the universe system: if these are so isomorphic as I keep telling myself, what really does the new kid on the block really bring? How much is a new incarnation better if their capabilities are so near? We should keep building cli tools, good cli tools, so that man and machine benefit.

That said I still leave the beads mcp server around. And I turn on the neovim MCP when I want to talk to neovim. Ah well. I should try harder to switch.

API vs MCP sounds like a real debate, but it really isn't. It's "API vs API discovery protocol." See how asinine it sounds if we call things for what they are.

With the CLI the agent could check out the project, work on it locally with its standard file editing / patching / reading tools, then push the work back to device. Run and debug on device, edit locally, push.

With MCP the agent had to query the MCP server for every read and write and was no longer operating in its normal coding loop. It still works, though, and as a user you can choose to bypass the CLI and connect directly via MCP.

The MCP server was valuable as it gave us a consistent and deterministic language to speak. The CLI tool + Skill was valuable for agentic coding because it allowed the coding work to happen with the standard editing tools used by agents.

The CLI also gave us device discovery. So the agent can simply discover nearby devices running Codea and get to work, instead of a user having to add a specific device via its IP address to their agent.

[1] https://codea.io/cli

Is MCP for in-house LLMs or can it work with ChatGPT as well? As far as I know it's a server with small self-contained task scripts. But don't get how the coordination works and how it's used.

There's your answer. If you want to use local tools, use Skills. If you want to use services, use MCP. Or, you know, whatever works best for your scenario.

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!

That's it. For some things you need MCP, for some things you need SKILLs - these things coexist.

I wanted to connect my Claude account to my Notion account. Apparently all you need to do is just submit the notion MCP and log in. That's it! And I was able to interact with my Notion data from my Claude account!

Imagine how hard this would be with skills? It is literally impossible because with skills, you may need to install some local CLI which Claude honestly should not allow.

If not CLI, you need to interact with their API which again can't happen because you can't authenticate easily.

MCP's fill this narrow gap in my opinion - where you don't own the runtime and you want to connect to other tools like plugins.

Each SKILLS.md will come with two hooks:

1. first for installing the SKILL itself - maybe install the CLI or do some initial work to get it working

2. Each skill may have dependencies on other skills - we need to install those first

Expressing these two hooks in a formal way in skills would help me completely replace MCP's.

My concrete prediction is that this will happen soon.

Wrote more about it here: https://simianwords.bearblog.dev/what-agent-skills-misses-no...

I’ve gone the other way, and used MCP-CLI to define all my MCP servers and wrap them in a CLI command for agent use. This lets me easily use them both locally and in cloud agents, without worrying about the harness support for MCP or how much context window will be eaten up. I have a minimal skill for how to use MCP-CLI, with progressive disclosure in the skill for each of the tools exposed by MCP-CLI. Works great.

All that said, I do think MCP will probably be the standard going forward, it just has too much momentum. Just need to solve progressive disclosure (like skills have!) and standardize some of the auth and transport layer stuff.

that's just me i guess.