Don't focus on what you prefer: it does not matter. Focus on what tool the LLM requires to do its work in the best way. MCP adds friction, imagine doing yourself the work using the average MCP server. However, skills alone are not sufficient if you want, for instance, creating the ability for LLMs to instrument a complicated system. Work in two steps:
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.
I 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.
This 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.
This isn't a zero-sum game or a choice of one over the other. They solve different layers of the developer experience: MCP provides a standardized, portable interface for external data/tools (the infrastructure), while Skills offer project-specific, high-level behavioral context (the orchestration). A robust workflow uses MCP to ensure tool reliability and Skills to define when and how to deploy those tools.
Scanning through the comments here I am almost certain the majority of people in this thread run coding agents on-device. Skills that access already available resources is then more convenient and you can easily make the argument that it is more agronomic.
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.
I agree for a slightly different reason - human stupidity.
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 feel like MCPs are encapsulation of multiple steps where the input to the first step is sufficient to drive the flow. Why would I spend tokens for the LLM to do reasoning at each of the steps when I can just provide the input + MCP call backed by a fixed program that can deal with the overall flow deterministically. If I have to do the same series of steps everytime, a script beats LLM doing the each step individually in terms of cost and time. If the flow involved some sort of fuzzy analysis or decision making in multiple places, I would probably let the LLM carry out the flow or break it into a combination of MCP calls orchestrated by the LLM.
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.
This frames MCP vs Skills as an either/or, but they operate at different layers. MCP exposes capabilities and Skills may shape how capabilities are used.
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].
Despite 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.
I like skills because they rely on the same tools which humans rely upon. A well-written skill can be read and used by a human too.
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.
> Context Bloat: 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. It’s like forcing someone to read the entire car’s owner’s manual when all they want to do is call car.turn_on().
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
For my use I prefer just a raw CLI. As long as it's built following conventions (e.g. using cobra for a Go app) then the agent will just natively know how to use it, by which I mean how to progressively learn what it needs by reading the help output. In that case you don't need a skill or anything. Just say "I want this information, use the xyz app". It will then try xyz --help or xyz help or a variant, just like a human would, see the subcommands, do xyz help subcommand and eventually find what it needs to do the job. Good tools provide an OAuth flow like xyz login, which will open a browser window where you can determine which resources you want to give the CLI (and thereby the agent) access to.
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.
One thing that I have found is that the platforms are surprisingly poor at consistently implementing MCP, which is actually a pretty simple protocol.
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.
Surprised to see no mention in the article or discussion yet about using MCPs in 'code mode', where an API is generated client-side relying on MCP primarily as an interface standard. I'm still learning but I've read this reduces the amount of context required to use the MCP.
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.
I've remained leaning a bit towards MCP until lately. Both have pretty easy ways to call the other (plenty of cli API callers, and tools like mcp-cli for the reverse https://github.com/philschmid/mcp-cli). Skills have really made progressive discovery if cli-tools much better, and MCP design has adapted likewise. I've lightly preferred MCP for formalism, for it feeling more concrete as a thing.
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.
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.
My biggest gripe with skills is that even clear and explicit instructions are regularly ignored - even when the skill is brief (< 100 lines).
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”
We had a contention between MCP / Skills for our product and ended up offering both. We built a CLI tool that could interface with the MCP server [1]. It seems redundant but our app is a coding app on iOS (Codea), and the issue with offering a plain MCP server meant that the agentic coding harness found it harder to do its job.
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.
For indie developers like myself, I often use chat GPT desktop and Claude desktop for arbitrary tasks, though my main workhorse is a customized coding harness with CC daemons on my nas. With the apps, b I missed having access to my Nas server where my dev environment is. So I wrote a file system MCP and hosted it with a reverse proxy on my Truenas with auth0. I wanted access to it from all platforms CharGPT mobile, desktop. Same for CC.
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?
The juxtaposition of MCP vs Skills in the article is very strange.
These are not competing ways to achieve something. Rather skills is often a way to enable an optimization on top of MCPs.
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.
Anthropic says that Skills and MCPs are complementary, and frankly the pure Skills zealots tend to miss that in enterprise environments you’ll have chatbots or the like that don’t have access to a full CLI. It doesn’t matter if your skills tell the agent exactly what to do if they can’t execute the commands. Also, MCP is better for restricted environments because you know exactly what it can or cannot do. That’s why MCP will exist for some time still. They solve distinct problem sets.
I think language grammars are the an interesting way to define a ruleset too. Forget REST API's or MCP Servers for a second... Define a domain specific language, and let the language model generate a valid instruction within the confines of that grammar.
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.
Really interesting post. The "connectors vs manuals" framing stuck with me because I think it points at something beyond the UX argument. A Skill that papers over an API loses the signal the friction was carrying. Working with a raw interface tells you something about the design.
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.
> 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.
Incorrect observation. Claude web does support skills upload. I guess claude runs code_interpreter tool and filesystem in the background to run user uploaded skills. ChatGPT business plans too allow uploading custom skills in web.
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?
We use MCP at work. In my team of about 6 people, everyone has Claude access, but about half of us are non-engineers. I built an MCP over our backend and Clickhouse, and setup a Claude Project with instruction (I'm assuming this count as skill?). The instruction is mostly for enriching the analytics data that we have, e.g. hinting Claude to prefer certain datasets for certain questions.
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).
Yesterday I accidentally stumbled on a place where I could really appreciate MCP's.
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.
The MCP vs skills debate feels like it's still very early days — I suspect we'll look back in a year and laugh at how much we debated this once the patterns become more obvious through real-world use.
I see the real argument is against poorly-designed MCP servers and where a skill/script would be a better fit.
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.
Every CLI can be expressed as an API and vice versa. Thus every skill can be expressed as an MCP server and vice versa. Any argument about the technical or practical merits of one over the other is willfully ignoring the fact that you can always use exactly the same patterns in one vs. the other.
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.
I prefer skills with simple curl commands. It's easy. You just need to create a server with HTTP endpoints and Claude (or other LLM) can call them with the curl commands you provide in your skills files. Claude is really good with curl and it's a well known HTTP client so what Claude is doing is more transparent to the user.
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/
From the article: "Sandboxing: Remote MCPs are naturally sandboxed. They expose a controlled interface rather than giving the LLM raw execution power in your local environment."
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.
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.
I love the idea of MCP, but it needs a progressive disclosure mechanism. A large MCP from a provider with hundreds or even thousands of tools can eat up a huge amount of your context window. Additionally, MCPs come in a bunch of different flavors in terms of transport and auth mechanisms, and not all harnesses support all those options well.
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.
I use both and don't feel they're mutually exclusive.
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 have vibe-coded 4 different software projects recently, on multiple platforms. I added search, RAG, ticketing, notifications, voice, and more features to them, in 2 minutes. All I had to do was implement MCP client, and suddenly all that other complex functionality "just worked", both locally and remotely.
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.
100%
MCPs truly give the agent tools and allow the agent to make better informed decisions given you can have configured the right MCP tools. Skills are good for knowledge and general guidelines. They give context to the agent, and I have seen some skills being excessively long that could into eat into the context window of the agent.
This tool https://protomcp.io/ helps a lot with testing MCP servers before integrating into the agent workflow. You can even see the agent call different tools in real time and view the trace.
Agreed on the MCP sentiment, and particularly remote MCPs. They keep themselves up to date with predefined tools, schemas, descriptions of how to use them, etc. where skills tend to be a mess over time, as described in the article.
Plug: If you want to try chatting with your financial data via an MCP, give FINTECH_MCP a try: https://www.fintechmcp.app - it's got a preview mode too so you can see how it works without linking any real data.
Experimenting with something adjacent — torget.ai, idea is an agent/tool marketplace so enjoyed this discussion :)
What strikes me is that MCP vs Skills vs Bespoke are all answers to ‘how does an agent use a known capability.’ How does it find one (?) is where I’m experimenting.
Discovery and payment at the agent layer still feels like the missing primitive.
Also (food for thought) the local LLM angle keeps getting underweighted in many discussions. For someone running Gemma 4 locally, there’s no tool layer at all by default. Different problem than the cloud agent angle.
I think the worse thing is when someone takes a clearly defined list of steps to do something and writes it as a skill rather than just having AI write it as a script. It’s like people have forgot what scripting is
The comparison to app stores is interesting but I think MCP registries solve a different problem. App stores are for humans browsing. MCP registries are for agents discovering tools at runtime based on the task at hand. The user never browses — they describe what they need and the agent finds the tool.
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.
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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.
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.
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.
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.
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.
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 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.
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.
> Context Bloat: 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. It’s like forcing someone to read the entire car’s owner’s manual when all they want to do is call car.turn_on().
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
helpoutput. In that case you don't need a skill or anything. Just say "I want this information, use the xyz app". It will then tryxyz --helporxyz helpor a variant, just like a human would, see the subcommands, doxyz help subcommandand eventually find what it needs to do the job. Good tools provide an OAuth flow likexyz login, which will open a browser window where you can determine which resources you want to give the CLI (and thereby the agent) access to.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...
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.
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.
> The core philosophy of MCP is simple: it’s an API abstraction. The LLM doesn’t need to understand the how; it just needs to know the what.
Wrong. It needs to "understand" both these things. The only difference is where and how the strings explaining them are generated.
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.
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”
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
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?
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.
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.
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 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?
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).
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.
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.
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.
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/
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.
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.
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.
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.
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.
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...
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
Plug: If you want to try chatting with your financial data via an MCP, give FINTECH_MCP a try: https://www.fintechmcp.app - it's got a preview mode too so you can see how it works without linking any real data.
What strikes me is that MCP vs Skills vs Bespoke are all answers to ‘how does an agent use a known capability.’ How does it find one (?) is where I’m experimenting.
Discovery and payment at the agent layer still feels like the missing primitive.
Also (food for thought) the local LLM angle keeps getting underweighted in many discussions. For someone running Gemma 4 locally, there’s no tool layer at all by default. Different problem than the cloud agent angle.
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.