Hacker News

> I tried to avoid writing this for a long time, but I'm convinced MCP provides no real-world benefit

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

MCPs (especially remote MCPs) are like a black box API -- you don't have to install anything, provision any resources, etc. You just call it and get an answer. There's a place for that, but an MCP is ultimately a blunt instrument.

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.

There is nothing wrong with MCP, it's just that stdio MCP was overengineered.

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.

Very good points, but, I think this blog is pretty focussed on the developer use case for LLMs. It makes a lot more sense in chat style interfaces for connecting to non-dev tools or services with non technical users, if anything just from a UX perspective.

This is like comparing OpenAPI and strings (that may be JSON). That is, weird, and possibly even meaningless.

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.

I can't believe everyone is talking about MCP vs CLI and which is superior; both are a method of tool calling, it does not matter which format the LLM uses for tool calling as long as it provides the same capabilities. CLIs might be marginably better (LLMs might have been trained on common CLIs), but MCPs have their uses (complex auth, connecting users to data sources) and in my experience if you're using any of the frontier models, it doesn't really matter which tool calling format you're using; a bespoke format also works.

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.

[0]: https://ampcode.com/manual#mcp-servers-in-skills

YES, I have been thinking the same and wrote a bit about it: https://vizzly.dev/blog/cli-json-output-llm-friendly/

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?

This feels misguided. MCP is still one of the best ways to execute deterministic sub-flows (i.e., stepwise processes) and secure tooling that an LLM would either lose itself while executing or should never access directly.

The token budget angle is what makes this a real architectural decision rather than a philosophical one.

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.

There's been an anti-MCP pro-CLI train going for a while since ~May of last year (I've been personally beating this drum since then) but I think MCP has a very real use case.

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.

I don't doubt that CLIs + skills are a good alternative to MCP in some contexts, but if you're building an app for non-developers and you need to let users connect it to arbitrary data sources there's really no sensible, safe path to using CLIs instead. MCP is going to be around for a long time, and we can expect it to get much better than it is today.

IMO the biggest issue with CLIs for agents is to know when the agent is allowed to type. When is the command fully proceed, and next tokens can now be generated.

I somehow agree with this but want to add my two cents here. Cloudflare's Codemode[0] is a great "replacement" for MCP because AI is trained for writing code and handling errors. But it also doesn't fix security and sandboxing. For CLI and file operations we have Vercel's just-bash[1] but for everything else there is no safe solution. Therefore MCP still makes sense until somebody sandboxes this part as well without needing to use Cloudflare or something.

[0]: https://developers.cloudflare.com/agents/api-reference/codem... [1]: https://github.com/vercel-labs/just-bash

Reports of MCP's demise have been greatly exaggerated, but a CLI is indeed the right choice when the interface to the LLM is not a chat in a browser window.

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.

[1] https://github.com/awebai/aw

I've been building tools that use both approaches and the answer really depends on the context.

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've yet to play with Emacs MCPs thoroughly. Having said that, after initial exposure to agent skills directing agents to just use CLI/emacsclient, I no longer think I need to go deeper into MCP. emacsclient via CLI has been working remarkably well. Did a little video on that https://www.youtube.com/watch?v=ymMlftdGx4I

The opposite is true. CLI based integration does not exist in a single consumer grade ai agent product that I’m aware of. CLI is only used in products like Claude Claude and OpenClaw that are targeting technically competent users.

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.

I've found MCP is great for stacking claude code instances- https://github.com/cduerr/stewardmcp has legit been a useful tool for me, and more reliable than handing a single instance 100k lines of code over a dozen repos.

As a counter argument to the kubectl example made in the article, I found the k8s MCP (https://github.com/containers/kubernetes-mcp-server) to be particularly usefuly in trying to restrict LLM access to certain tools such as exec and delete tools, something which is not doable out of box if you use the kubectl CLI (unless you use the --as or --as-group flags and don't tell the LLM what user/usergroup those are).

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.

MCP makes sense when you're not running a full container-based Unix environment for your agent to run Bash commands inside of.

I've been thinking about this a lot lately in terms of my personal clauding, and it's hard for me to think of a scenario where an mcp server makes more sense than CLI tools and AGENTS.md. But if you're deploying an agentic product, it's probably different. Seems like you could just deploy little Bash sandboxes for each customer. Or you could deploy mcp servers. The latter feels much easier to reason about in terms of attack surface and potential side effects.

MCPs are useful to deploy internally as an agent tool gateway for your organization or customers. That's a totally different use case than how most of us interact with agents (claude code / cursor). That said, there's only limited benefit over just using OpenAPI.

This will happen with GUIs as well, once computer-use agents start getting good. Why bother providing an API, when people can just direct their agent to click around inside the app? Trillions of matmuls to accomplish the same result as one HTTP request. It will be glorious. (I am only half joking...)

If the author is just using Claude Code on their own personal computer, they can do whatever they want.

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.

To be fair to MCP it came out 150 years ago, in November 2024. Agents were running on steam and coal then with Sam Altman shovelling the furnace.

For me, GitHub CLI is the prime example of this. This CLI is so incredibly powerful when combined with regular command line tools. Agents know how to use head, tail, jq and so on to only extract the parts it needs.

The best selling point of CLIs is the ability to chain, transform and combine. MCP cannot do this.

I think if you want background agents with sandboxes and well scoped permissions, you want MCP to be your data protocol and security layer.

If you’re vibing and doing the open claw thing without any security concerns; then you’re absolutely right.

The CLI vs MCP debate is about input ergonomics — how the agent invokes tools. Both sides are arguing about the left side of the pipeline.

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.

When we give LLM access to the terminal, LLM doesn't need anything else. LLM already knows all the commands, and there's no need to waste context on MCP. When we tell it to use a new command, it can get `--help` as needed.

Remote MCP solve the distribution problem just like everyone uses web apps for everything nowadays instead of desktop apps. Local MCP servers make as much sense as local web apps.

I've come to the same conclusion as op, created a CLI tool to work with Chrome sessions. It works well, and I'm planning to do some token comparison on this vs an MCP approach. https://news.ycombinator.com/item?id=47207790

It really seems the primary benefit of MCP servers was to force companies to establish some externally exposed APIs if/when they did not have them.

MCP has one thing going for it as an agentic API standard: token efficiency

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.

I keep asking why the default Claude tools like Read(), Write(), Edit(), MultiEdit(), Replace() tools aren’t just Bash() with some combination of cat, sed, grep, find. Isn’t it just easier to pipe everything through the shell? We just need to figure out the permissions for it.

MCPs were always for chat apps, not coding agents. Benefit is auth for normies + can serve UI through ChatGPT + Claude.

I’ve been using the command line tools except for teams integration which I use MCP for

Why choose if you can have both? You can turn any MCP into an CLI with Pete's MCPorter: https://mcporter.dev.

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.

genuine question: how would you turn a debugger-mcp into a CLI? do CLI tools used in agents have the concept of persistence?

?? I'm using my own remote MCP server with openclaw now. I do understand the use case for CLI. In his Lex Friedman interview the creator highlights some of the advantages of CLI, such as being able to grep over responses. But there are situations where remote MCP works really well, such as where OAuth is used for authentication - you can hit an endpoint on the MCP server, get redirected to authenticate and authorise scopes etc and the auth server then responds to the MCP server.

https://github.com/mcpshim/mcpshim - converts MCPs to CLI - best of both worlds

on the other hand, MCP/tools style lets me run specialized agents without needing to give them a bash-like unix environment. Vercel makes a fake bash simulator but if i’m trying to run an agent that analyzes production telemetry, it’s a hell of a lot easier to do that safely with just MCP

For personnal agents like claude code, clis are awesome.

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.

Tell my business users to use CLI when they create their agents. It's just not happening. MCP is point-and-click for them.

MCP is far from dead, at least outside of tech circles.

In terms of what companies are actually implementing, MCP isnt dead by a long time. Number of companies with a MCP server grew 242% in the last 6 months and is actually accelerating (according to Bloomberry) [1]

https://bloomberry.com/blog/we-analyzed-1400-mcp-servers-her...

Advancing capability in the models themselves should be expected to eat alive every helpful harness you create to improve its capabilities.

And for anything at all complicated, what’s even better than a CLI is a JS or Python library so the thing can just write code.

> OpenClaw doesn’t support it. Pi doesn’t support it.

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.

Hi friends! Author here. This blew up a bit, so some words.

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).

This doesn't really pay attention to token costs. If I'm making a series of statically dependent calls I want to avoid blowing up the context with information on the intermediary states. Also, I don't really want to send my users skill.md files on how to do X,Y & Z.

This is actually the first use case where I agree with the poster. really interesting, especially for technical people using ai. why would you spend time setting up and installing an mcp server when u can give it one man page

I don't understand the CLI vs MCP. In cli's like Claude Code, MCPs give a lot of additional functionality, such as status polling that is hard to get right with raw documentation on what APIs to call.

Biggest downside of CLI for me is that it needs to run in a container. You're allowing the agent to run CLI tools, so you need to limit what it can do.

Thoughts on Agent Context Protocol (ACP)?

Couldn't agree more. Simple REST APIs often do the job as well. MCP felt like a vibe-coded fever dream from the start.

I don't know about this. I use AI, but I've never used or tried MCP. I've never had any problems with the current tools.

MCPs were dead in the water and were completely a bad standard to begin with. The hype around never made sense.

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.

[0] https://news.ycombinator.com/item?id=44528411

what honestly is the difference between an mcp and a skill + instructions + curl.

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?

First they came for our RAGs, now for our MCPs. What’s next ?

[dead]

[dead]

[dead]

This got renamed right in front of my eyes

I remember this kind of people against Kubernetes the same exact way. Very funny.