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.

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...

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

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

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.

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

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

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.

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.

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.

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

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”

    > 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?

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?

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

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.

- "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.

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

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!

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.

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...

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.

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.