CLI tools on the other hand are like precision instruments. Yes, you have to install them locally once, but after that, they have access to your local environment and can discover things on their own. There are two CLIs are particularly powerful for working with large structured data: `jq` and `duckdb` cli. I tell the agent to never load large JSON, CSV or Parquet files into context -- instead, introspect them intelligently by sampling the data with said CLI tools. And Opus 4.6 is amazing at this! It figures out the shape of the data on its own within seconds by writing "probing" queries in DuckDB and jq. When it hits a bottleneck, Opus 4.6 figures out what's wrong, and tries other query strategies. It's amazing to watch it go down rabbit holes and then recovering automatically. This is especially useful for doing exploratory data analysis in ML work. The agent uses these tools to quickly check data edge cases, and does a way more thorough job than me.
CLIs also feel "snappier" than MCPs. MCPs often have latency, whereas you can see CLIs do things in real time. There's a certain ergonomic niceness to this.
p.s. other CLIs I use often in conjunction with agents:
`showboat` (Simon Willison) to do linear walkthroughts of code.
`br` (Rust port of Beads) to create epics/stories/tasks to direct Opus in implementing a plan.
`psql` to probe Postgres databases.
`roborev` (Wes McKinney) to do automatic code reviews and fixes.
or install Docker and have the agent run CLI commands in docker containers that mount the local directory. That way you essentially never have to install anything. I imagine there's a "skill" that you could set up to describe how to use docker (or podman or whatever) for all CLI interactions, but I haven't tried yet.
I do believe that as vision/multi-modal models get to a better state, we would see even crazier interaction surfaces.
RE: duckdb. I have a wonderful time with ChatGPT talking to duckdb but I have kept it to inmemory db only. Do you set up some system prompt that tell it to keep a duckdb database locally on disk in the current folder?
No, I don't use DuckDB's database format at all. DuckDB for me is more like an engine to work with CSV/Parquet (similar to `jq` for JSON, and `grep` for strings).
Also I don't use web-based chat (you mentioned ChatGPT) -- all these interactions are through agents like Kiro or Claude Code.
I often have CSVs that are 100s of MBs and there's no way they fit in context, so I tell Opus to use DuckDB to sample data from the CSV. DuckDB works way better than any dedicated CSV tool because it packs a full database engine that can return aggregates, explore the limits of your data (max/min), figure out categorical data levels, etc.
For Parquet, I just point DuckDB to the 100s of GBs of Parquet files in S3 (our data lake), and it's blazing fast at introspecting that data. DuckDB is one of the best Parquet query engines on the planet (imo better than Apache Spark) despite being just a tiny little CLI tool.
One of the use cases is debugging results from an ML model artifact (which is more difficult that debugging code).
For instance, let's say a customer points out a weird result in a particular model prediction. I highlight that weird result, and tell Opus to work backwards to trace how the ML model (I provide the training code and inference code) arrived at that number. Surprisingly, Opus 4.6 is does a great job using DuckDB to figure out how the input data produced that one weird output. If necessary, Opus will even write temporary Python code to call the inference part of the ML model to do inference on a sample to verify assumptions. If the assumptions turn out to be wrong, Opus will change strategies. It's like watching a really smart junior work through the problem systematically. Even if Opus doesn't end up nailing the actual cause, it gets into the proximity of the real cause and I can figure out the rest. (usually it's not the ML model itself, but some anomaly in the input). This has saved me so much time in deep-diving weird results. Not only that, I can have confidence in the deep-dive because I can just run the exact DuckDB SQL to convince myself (and others) of the source of the error, and that it's not something Opus hallucinated. CLI tools are deterministic and transparent that way. (unlike MCPs which are black boxes)
MCP's Streamable HTTP with OAuth discovery is the best way to ship AI integration with your product nowadays. CLIs require sandboxing, doesn't handle auth in a standard way and it doesn't integrate to ChatGPT or Claude.
Look at Sentry, they just ship a single URL https://mcp.sentry.dev/mcp and you don't need anything else. All agents that supports MCP lets you click a link to login to Sentry and they make calls to Sentry to fetch authentificated data.
The main problem with MCP is the implementation. Instead of using bash to call MCP, agents are designed to make single MCP tool calling which doesn't allow composability. We solve this problem with exposing MCP tools as HTTP endpoints and it works like charm.
Way easier to set up, centralized auth and telemetry.
Just use it for the right use cases.
Developers have a rich set of CLIs because they live in the terminal and built those tools for themselves.
Imagine your favorite email provider has a CLI for reading and sending email - you're cool with the agent reading, but not sending. What are you going to do? Make 2 API keys? Make N API keys for each possible tool configuration you care about?
MCPs make this problem simple and easy to solve. CLIs don't.
I don't think OpenClaw will last that long without security solved well - and MCPs seem to be obvious solution, but actively rejected by that community.
MCP is formally defined in the general sense (including transport protocols), CLI is not. I mean, only specific CLIs can be defined, but a general CLI is only `(String, List String, Map Int Stream) -> PID` with no finer semantics attached (save for what the command name may imply), and transport is “whatever you can bring to make streams and PIDs work”. One has to use `("cli-tool", ["--help"], {1: stdout})` (hoping that “--help” is recognized) to know more. Or use man/info (if the CLI ships a standardized documentation), or some other document.
But in the they’re both just APIs. If the sufficient semantics is provided they both do the trick.
If immediate (first-prompt) context size is a concern, just throw in a RAG that can answer what tools (MCPs or CLIs or whatever) exist out there that could be useful for a given task, rather than pushing all the documentation (MCP or CLI docs) proactively. Or, well, fine tune so the model “knows” the right tools and how to use them “innately”.
Point is, what matters is not MCP or CLI but “to achieve X must use F [more details follow]”. MCP is just a way to write this in a structured way, CLIs don’t magically avoid this.
> MCP is just a way to write this in a structured way,
Nope! You are not understanding or are actively ignoring the difference which has been explained by 20+ comments just here. It's not a controversial claim, it's a mutually agreed upon matter of fact by the relevant community of users.
The claim you're making right now is believed to be false, and if you know something everyone else doesn't, then you should create an example repo that shows the playwright CLI and playwright MCP add the same number of tokens to context and that both are equally configurable in this respect.
If you can get that right where so many others have failed, that would be a a really big contribution. And if you can't, then you'll understand something first-hand that you weren't able to get while you were thinking about theoretically.
That's just implementation detail of how your agent harness decides to use MCP. CLI and MCP are on different abstraction layers. You can have your MCP available through CLI if you wish so.
Then there things like browser-mcp and some tools you make yourself to make workflow easier (adding self-describing MCP is easier than telling agent about your CLI tool in some markdown file that LLM forgot about 20k tokens ago.
The difference that should be talked about, should be how skills allow much more efficient context management. Skills are frequently connected to CLI usage, but I don't see any reason why. For example, Amp allows skills to attach MCP servers to them – the MCP server is automatically launched when the Agent loads that skill[0]. I belive that both for MCP servers and CLIs, having them in skills is the way for efficent context, and hoping that other agents also adopt this same feature.
That's fine if you definition of capabilities is wide enough to include model understanding of the provided tool and token waste in the model trying to understand the tool and token waste in the model doing things ass backwards and inflating the context because it can't see the vastly shorter path to the solution provided by the tool and...
There is plenty of evidence to suggest that performance, success rates, and efficiency, are all impacted quite drastically by the particular combination of tool and model.
This is evidenced by the end of your paragraph in which you admit that you are focused only on a couple (or perhaps a few) models. But even then, throw them a tool they don't understand that has the same capabilities as a tool they do understand and you're going to burn a bunch of tokens watching it try to figure the tool out.
Tooling absolutely matters.
> But even then, throw them a tool they don't understand that has the same capabilities as a tool they do understand and you're going to burn a bunch of tokens watching it try to figure the tool out.
What I was trying to say was that this applies to both MCPs and CLIs – obviously, if you have a certain CLI tool that's represented thoroughly through the model's training dataset (i.e. grep, gh, sed, and so on), it's definitely beneficial to use CLIs (since it means less context spending, less trial-and-error to get the expected results, and so on).
However if you have a novel thing that you want to connect to LLM-based Agents, i.e. a reverse enginnering tool, or a browser debugging protocol adapter, or your next big thing(tm), it might not really matter if you have a CLI or a MCP since LLMs are both post-trained (hence proficent) for both, and you'll have to do the trial-and-error thing anyway (since neither would represented in the training dataset).
I would say that the MCP hype is dying out so I personally won't build a new product with MCP right now, but no need to ditch MCPs for any reason, nor do I see anything inherently deficient in the MCP protocol itself. It's just another tool-calling solution.
MCP tool calls aren't composable. Not the same capabilities. Big difference.
That does seem very powerful now that I've had some time to think about it.
I do think that we should adopt Amp's MCPs-on-skills model that I've mentioned in my original comment more (hence allowing on-demand context management).
Eventually I hope that models themselves become smarter and don't save the whole 54k tokens in their context window
The main problem with this approach at the moment is it busts your prompt cache, because LLMs expect all tool definitions to be defined at the beginning of the context window. Input tokens are the main driver of inference costs and a lot of use cases aren't economical without prompt caching.
Hopefully in future LLMs are trained so you can add tool definitions anywhere in the context window. Lots of use cases benefit from this, e.g. in ecommerce there's really no point providing a "clear cart" tool to the LLM upfront, it'd be nice if you could dynamically provide it after item(s) are first added.
TBH I'm not really sure how it works in Amp (I never actually inspected how it alters the prompts that are sent to Anthropic), but does it really matter for the LLMs to have the tool definitions at the beginning of the context window in contrast to the bottom before my next new prompt?
I mean, skills also work the same way, right? (it gets appended at the bottom, when the LLM triggers the skill) Why not MCP tooling definitions? (They're basically the same thing, no?)
Thank you so much to the GH CLI for making me realize this, really. The only MCPs I use still are ones that don’t have CLIs. Hell, I even just wrote a CLI for Bear Notes, for LLMs. It’s just better.
Seems like the last MCP use case is model to model communication but I’m sure others have approach’s for that?
MCPs have provided any easy way to side-step that baggage.
e.g. in an MCP, you have tools, those tools are usually binned into "read" vs "write". Given that, I can easily configure my tooling to give an LLM (e.g. Claude Code) unlimited read access to some system (by allowing all read-only tools) without likewise giving the LLM write/destructive access.
Obviously you can design APIs/CLIs with this in mind, but up until now that has not been a primary concern so they haven't.
I've been using both approaches in projects and the pattern I've landed on: MCP for anything stateful (db connections, authenticated sessions, browser automation) and CLI for stateless operations where the output is predictable. The reason is simple - MCP tool definitions sit in context permanently, so you're paying tokens whether you use them or not. A CLI you can invoke on demand and forget.
The discovery aspect is underrated though. With MCP the model knows what tools exist and what arguments they take without you writing elaborate system prompts. With CLI the model either needs to already know the tool (grep, git, curl) or you end up describing it anyway, which is basically reinventing tool definitions.
Honestly the whole debate feels like REST vs GraphQL circa 2017. Both work, the answer depends on your constraints, and in two years we'll probably have something that obsoletes both.
Specifically, MCP is a great unit of encapsulation. I have a secure agent framework (https://github.com/sibyllinesoft/smith-core) where I convert MCPs to microservices via sidecar and plug them into a service mesh, it makes securing agent capabilities really easy by leveraging existing policy and management tools. Then agents can just curl everything in bash rather than needing CLIs for everything. CLIs are still slightly more token efficient but overall the simplicity and the power of the scheme is a huge win.
Which is not a high bar to clear. It literally only got where it is now because execs and product people love themselves another standard, because if they get their products to support it they can write that on some excel sheet as shipped feature and pin it on their chest. Even if the standard sucks on a technical level and the spec changes all the time.
For example, I built https://claweb.ai to enable agents to communicate with other agents. They run aw [1], an OSS Go CLI that manages all the details. This means they can have sync chats (not impossible with MCP, but very difficult). It also enables signing messages and (coming soon) e2ee. This would be, as far as I can tell, impossible using MCP.
[0]: https://developers.cloudflare.com/agents/api-reference/codem... [1]: https://github.com/vercel-labs/just-bash
For the other 99% of the population, MCP offers security guardrails and simple consistent auth. Much better than CLI for the vast majority of use cases involving non-technical people.
MCP shines when you need stateful, multi-step interactions - things like browsing a codebase, running tests iteratively, or managing deployment pipelines where each step depends on the last.
CLI wins when the task is well-defined and atomic. "Run this audit", "deploy this thing", "format this file." No ambiguity, no state to maintain.
The trap I see people falling into: using MCP for everything because it's new and shiny, when a simple CLI wrapper would be faster, more reliable, and easier to debug. The best tools I've built combine both - CLI for the happy path, MCP for the exploratory/interactive path.
I have used the kk8s MCP directly inside Github Copilot Chat in VSCode and restricted the write tools in the Configure Tools prompt. With a pseudo protocol established via this MCP and the IDE integration, I find it much safer to prompt the LLM into debugging a live K8s cluster vs. without having any such primitives.
So I don't see why MCPs are or should be dead.
I want to be able to give agents access to computation in a secure way without giving them full access to a computer
As soon as there is a need to interact with the outside world in a safe, controlled manner at enterprise scale, the limitations of CLI quickly become obvious.
I wish people get more informed about a subject before they write a long blog post about it.
The best selling point of CLIs is the ability to chain, transform and combine. MCP cannot do this.
If you’re vibing and doing the open claw thing without any security concerns; then you’re absolutely right.
The single-request-for-all-abilities model + JSON RPC is more token efficient than most alternatives. Less flexible in many ways, but given the current ReAct, etc. model of agentic AI, in which conversations grow geometrically with API responses, token efficiency is very important.
It could just be fixed to compress the context or the protocol could be tweaked.
Switching to CLIs is like buying a new car because you need an oil change. Sure, in this case, the user doesn’t get to control if the oil change can be done, but the issue is not the car — it’s that no one will do the relatively trivial fix.
I say this as a hypermedia enthusiast who was hoping to show otherwise.
The harder unsolved problem is the right side: what happens to the output before it becomes consequential action. Neither a CLI nor an MCP server tells you whether the text the agent just generated is compliant, scoped, or admissible. That enforcement problem exists regardless of which invocation pattern you prefer.
The best CLI in the world doesn't help you when the agent produces a clinical summary that omits a contraindication or a financial disclosure that drifts outside regulatory bounds. That's a different layer entirely — and it's mostly being ignored while everyone argues about transport protocols.
Since I've just switched from buggy Claude Code to pi, I created an extension for it: https://github.com/mavam/pi-mcporter.
There are still a few OAuth quirks, but it works well.
MCP is far from dead, at least outside of tech circles.
In web/cloud based environment, giving a cli to the agent is not easy. Codemode comes to mind but often the tool is externalized anyway so mcp comes handy. Standardisation of auth makes sense in these environments too.
https://bloomberry.com/blog/we-analyzed-1400-mcp-servers-her...
It's maybe not optimal to conclude anything from these two. The Vienna school of AI agents focuses on self extending agents and that's not really compatible with MCP. There are lots of other approaches where MCP is very entrenched and probably will stick around.
The article title and content is intentionally provocative. It’s just to get people thinking. My real views are probably a lot more balanced. I totally get there’s a space where MCP probably does actually make sense. Particularly in areas where CLI invocation would be challenging. I think we probably could have come up with something better than MCP to fill that space, but it’s still better than nothing.
Really all I want folks to take away from this is to think “hmm, maybe a CLI would actually be better for this particular use case”. If I were to point a finger at anything in particular, it would be Datadog and Slack who have chosen to build MCP’s instead of official CLI’s that agents can use. A CLI would be infinitely better (for me).
I would almost use the words "intentionally uninformed" instead.
There are huge holes in the article (as pointed out by many comments here), and I have to wonder if you genuinely don't have enough experience with MCP to bring them up, or you intentionally omitted them to make the arguments for CLI.
You need agent to find MCP and what it can be used for (context), similarly you can write what CLI use for e.g. jira.
Rest is up to agent, it needs to list what it can do in MCP, similarly CLI with proper help text will list that.
Regarding context those tools are exactly the same.
When measuring speed running blue team CTFs ("Breaking BOTS" talk at Chaos Congress), I saw about a ~2x difference in speed (~= tokens) for a database usage between curl (~skills) vs mcp (~python). In theory you can rewrite the mcp into the skill as .md/.py, but at that point ... .
Also I think some people are talking past one another in these discussions. The skill format is a folder that supports dropping in code files, so much of what MCP does can be copy-pasted into that. However, many people discussing skills mean markdown-only and letting the LLM do the rest, which would require a fancy bootstrapping period to make as smooth as the code version. I'd agree that skills, when a folder coming with code, does feel like largely obviating MCPs for solo use cases, until you consider remote MCPs & OAuth, which seem unaddressed and core in practice for wider use.
It breaks most assumptions we have about the shell's security model.
Not only it had lots of issues and security problems all over the place and it was designed to be complicated.
For example, Why does your password manager need an MCP server? [0]
But it still does not mean a CLI is any better for everything.
Really it seems to me the difference is that an mcp could be more token-efficient, but it isn't, because you dump every mcp's instructions all the time into your context.
Of course then again skills frequently doesn't get triggered.
just seems like coding agent bugs/choices and protocol design?
MCP is still going to be handy enough for iot type devices, where an llm can discover what's actually supported by that device without needing to query about the specific version.
Swagger / OpenAPI just aren't detailed enough to use without other documentation.
Skills & instructions will always have the limit that they run locally, so if they don't match the server there is a problem.
IMO this is 100% correct and I'm glad someone finally said it. I run AI agents that control my entire dev workflow through shell commands and they are shockingly good at it. the agent figures out CLI flags it has never seen before just from --help output. meanwhile every MCP server i've used has been a flaky process that needs babysitting.
the composability argument is the one that should end this debate tbh. you can pipe CLI output through jq, grep it, redirect to files - try doing that with MCP. you can't. you're stuck with whatever the MCP server decided to return and if it's too verbose you're burning tokens for nothing.
> companies scrambled to ship MCP servers as proof they were "AI first"
FWIW this is the real story. MCP adoption is a marketing signal not a technical one. 242% growth in MCP servers means nothing if most of them are worse than the CLI that already existed
I don't know, to me it seems like the LLM cli tools are the current pinnacle. All the LLM companies are throwing a ton of shit at the wall to see what else they can get to stick.
My use case was for using it as an advanced search tool rather than for creating tickets or documentation. Considering how poor the Confluence search function is, the results from Confluence via an MCP-powered search are remarkably good. I was able to solve one or two obscure, company-specific issues purely by using the MCP search, and I'm convinced that finding these pages would have been almost impossible without it.
That said the core argument for MCP servers is providing an LLM a guard-railed API around some enterprise service. A gmail integration is a great example. Without MCP, you need a VM as scratch space, some way to refresh OAuth, and some way to prevent your LLM from doing insane things like deleting half of your emails. An MCP server built by trusted providers solves all of these problems.
But that's not what happened.
Developers and Anthropic got coked up about the whole thing and extended the concept to nuts and bolts. I always found the example servers useless and hilarious.[0] Unbelievably, they're still maintained.
[0]: https://github.com/modelcontextprotocol/servers/tree/main/sr...
MCP servers were also created at a time where ai and llms were less developed and capable in many ways.
It always seemed weird we'd want to post train on MCP servers when I'm sure we have a lot of data with using cli and shell commands to improve tool calling.
But even those are not better for agent use than the human cli counterpart.