There is some important context missing from the article.
First, MCP tools are sent on every request. If you look at the notion MCP the search tool description is basically a mini tutorial. This is going right into the context window. Given that in most cases MCP tool loading is all or nothing (unless you pre-select the tools by some other means) MCP in general will bloat your context significantly. I think I counted about 20 tools in GitHub Copilot VSCode extension recently. That's a lot!
Second, MCP tools are not compossible. When I call the notion search tool I get a dump of whatever they decide to return which might be a lot. The model has no means to decide how much data to process. You normally get a JSON data dump with many token-unfriendly data-points like identifiers, urls, etc. The CLI-based approach on the other hand is scriptable. Coding assistant will typically pipe the tool in jq or tail to process the data chunk by chunk because this is how they are trained these days.
If you want to use MCP in your agent, you need to bring in the MCP model and all of its baggage which is a lot. You need to handle oauth, handle tool loading and selection, reloading, etc.
The simpler solution is to have a single MCP server handling all of the things at system level and then have a tiny CLI that can call into the tools.
In the case of mcpshim (which I posted in another comment) the CLI communicates with the sever via a very simple unix socket using simple json. In fact, it is so simple that you can create a bash client in 5 lines of code.
This method is practically universal because most AI agents these days know how to use SKILLs. So the goal is to have more CLI tools. But instead of writing CLI for every service you can simply pivot on top of their existing MCP.
This solves the context problem in a very elegant way in my opinion.
So basically the best way to use MCP is not to use it at all and just call the APIs directly or through a CLI. If those dont exist then wrapping the MCP into a CLI is the second best thing.
This was my initial understanding but if you want ai agents to do complex multi step workflows I.e. making data pipelines they just do so much better with MCP.
After I got the MCP working my case the performance difference was dramatic
I have never had a problem using cli tools intead of mcp. If you add a little list of the available tools to the context it's nearly the same thing, though with added benefits of e.g. being able to chain multiple together in one tool call
Or write your own MCP server and make lots of little tools that activate on demand or put smarts or a second layer LLM into crafting GQL queries on the fly and reducing the results on the fly. They're kinda trivial to write now.
I do agree that MCP context management should be better. Amazon kiro took a stab at that with powers
SQL is peak for data retrieval (obviously) but challenging to deploy for multitenant applications where you can't just give the user controlled agent a DB connection. I found it every effective to create a mini paquet "data ponds" on the fly in s3 and allow the agent to query it with duckdb (can be via tool call but better via a code interpreter). Nice thing with this approach is you can add data from any source and the agent can join efficiently.
> Before your agent can do anything useful, it needs to know what tools are available. MCP’s answer is to dump the entire tool catalog into the conversation as JSON Schema. Every tool, every parameter, every option.
Because this simply isn't true anymore for the best clients, like Claude Code.
Similar to how Skills were designed[1] to be searchable without dumping everything into context, MCP tools can (and does in Claude Code) work the same way.
If we use prompt caching - isn't a largish MCP tools section just like a fixed token penalty in return for higher speed at runtime, because tools don't need to be discovered on demand, and that's the better tradeoff? At least for the most powerful models it doesn't feel like their quality goes down much with a few MCP servers. I might be missing something.
Does tool calling in general bloat context, or is there something particular about MCP?
One thing I have read recently is that when you make a tool call it forces the model to go back to the agent. The effect of this is that the agent then has to make another request with all of the prompt (include past messages), these will be "cached" tokens, but they're still expensive. So if you can amortize the tool calls by having the model either do many at once or chaining them with something like bash you'll be better off.
I suspect this might be why cursor likes writing bash scripts so much, simple shell commands are going to be very token heavy because of the frequency of interrupts.
I actually want to combine this and CLIHub into a directory where someone can download all the official MCPs or CLIs (or MCP to CLIs) with a single command
After reading Cloudflare's Code Mode MCP blog post[1] I built CMCP[2] which lets you aggregate all MCP servers behind two mcp tools, search and execute.
I do understand anthropic's Tool Search helps with mcp bloat, but it's limited only to claude.
CMCP currently supports codex and claude but PRs are welcome to add more clients.
TL;DR
CLIHUB compiles MCP servers into portable, self-contained binaries — think of it like a compiler. Best for distribution, CI, and environments where you can't run a daemon.
mcpshim is a runtime bridge — think of it like a local proxy. Best for developers juggling many MCP servers locally, especially when paired with LLM agents that benefit from persistent connections and lightweight aliases.
One important aspect of mcpshim which you might want to bring into clihub is the history idea. Imagine if the model wants to know what it did couple of days ago. It will be nice to have an answer for that if you record the tool calls in a file and then allow the agent to query the file.
I'm looking at this from a slightly different level of abstraction.
The CLI approach definitely has practical benefits for token reduction. Not stuffing the entire schema into the runtime context is a clear win. But my main interest lies less in "token cost" and more in "how we structure the semantic space."
MCP is fundamentally a tool-level protocol. Existing paradigms like Skills already mitigate context bloat and selection overhead pretty well via tool discovery and progressive disclosure. So framing this purely as "MCP vs CLI" feels more like shifting the execution surface rather than a fundamental architectural shift.
The direction I'm exploring is a bit different. Instead of treating tools as the primary unit, what if we normalize the semantic primitives above them (e.g., "search," "read," "create")? Services would then just provide a projection of those semantics. This lets you compress the semantic space itself, expose it lazily, and only pull in the concrete tool/CLI/MCP adapters right at execution time.
You can arguably approximate this with Skills, but the current mental model is still heavily anchored to "tool descriptions"—it doesn't treat normalized semantics as first-class citizens. So while the CLI approach is an interesting optimization, I'm still on the fence about whether it's a real structural paradigm shift beyond just saving tokens.
Ultimately, shouldn't the core question be less about "how do we expose fewer tools," and more about "how do we layer and compress the semantic space the agent has to navigate?"
Haha I agree that my opinion is kind of that
But more like ports & adapters for semantic space, not just IO boundaries.
If we can abstract the tools one layer further for ai, it might reduce the attention it needs to spend navigating them and leave more context window for actual reasoning
True for coding agents running SotA models where you're the human-in-the-loop approving, less true for your deployed agents running on cheap models that you don't see what's being executed.
Probably oversold here because if you read the fine print, the savings only come in cases when you don't need the bytes in context.
That makes sense for some of the examples the described (e.g. a QA workflow asking the agent to take a screenshot and put it into a folder).
However, this is not true for an active dev workflow when you actually do want it to see that the elements are not lining up or are overlapping or not behaving correctly. So token savings are possible...if your use case doesn't require the bytes in context (which most active dev use cases probably do)*
I feel like the permanent fix is for the AI labs to figure out better attention methods that increase context length without extra inference cost, plus deeper discounts (like -99%) for people being able to add system prompts to their accounts that are cached permanently.
This way you build all your MCPs into the system prompt, save the prompt to the AI provider, then use it without overpaying API costs.
The current "tools-on-demand" workarounds should be great for infrequent tools but the future will probably bring agents with dozens of tools that need them in context to flexibly many of them in the same context window. So we just need to make the context windows longer and make this capability cheaper to use.
Not just cheaper in terms of token usage but accuracy as well.
Even the smallest models are RL trained to use shell commands perfectly. Gemini 3 flash performs better with a cli with 20 commands vs 20+ tools in my testing.
cli also works well in terms of maintaining KV cache (changing tools mid say to improve model performance suffers from kv cache vs cli —help command only showing manual for specific command in append only fashion)
Writing your tools as unix like cli also has a nice benefit of model being able to pipe multiple commands together. In the case of browser, i wrote mini-browser which frontier models use much better than explicit tools to control browser because they can compose a giant command sequence to one shot task.
I’m not sure how this works. A lot of that tool description is important to the Agent understanding what it can and can’t do with the specific MCP provider. You’d have to make up for that with a much longer overarching description. Especially for internal only tools that the LLM has no intrinsic context for.
These days you can rewrite everything yourself for very cheap. So this is `mcporter` rewritten. I prefer to use Rust personally for rewrites. Opus 4.6 can churn it out pretty quickly if that's what you want. To be honest, almost all software that I want to try these days I don't even install. Instead I'd rather read the README and produce a personal version. This allows encoding idiosyncrasies and specifics that another author will not accept.
MCP defines a consistent authentication protocol. This is the real issue with CLIs, each CLI can (and will) have a different way of handling authentication (env variables, config set, JSON, yml, etc).
But tbh there's no reason agents can't abstract this out. As long as a CLI has a --help or similar (which 99% do) with a description of how to login, then it can figure it out for you. This does take context and tool calls though so not hugely efficient.
MCP has some schemas though. CLI is a bit of a mess.
But MCP today isn’t ideal. I think we need to have some catalogs where the agents can fetch more information about MCP services instead of filling the context with not relevant noise.
So much incorrect and misinformation in these comments. As someone who is building an agent[0] with MCP tools, neither the MCP tool description nor the response is the problem. Both of those are easily solved by not bloating them.
The real killer is the input tokens on each step. If you have 100k tokens in the conversation, and the LLM calls an MCP tool, the output and the existing conversation is sent back. So now you've input 200k tokens to the LLM.
Now imagine 10 tool calls per user message - or 50. You're sending 1-5M input tokens, not because the MCP definitions or tool responses are large, but because at each step, you have to send the whole conversation again.
"what about caching" - Only 90% savings, also cache misses are surprisingly common (we see as low as 40% cache hit rate)
"MCP definitions are still large" - not compared to any normal conversation. Also these get cached
We've seen the biggest savings by batching/parallelizing tool calls. I suspect the future of LLM tool usage will have a different architecture, but CLI doesn't solve the problems either.
But this is just the nature of LLMs (so far). Every "conversation" involves sending the entire conversation history back.
The article misses imo the main benefit of CLIs vs _current_ MCP implementations [1], the fact that they can be chained together with some sort of scripting by the agent.
Imagine you want to sum the total of say 150 order IDs (and the API behind the scenes only allows one ID per API calls).
With MCP the agent would have to do 150 tool calls and explode your context.
With CLIs the agent can write a for loop in whatever scripting language it needs, parse out the order value and sum, _in one tool call_. This would be maybe 500 tokens total, probably 1% of trying to do it with MCP.
[1] There is actually no reason that MCP couldn't be composed like this, the AI harnesses could provide a code execution environment with the MCPs exposed somehow. But noone does it ATM AFIAK. Sort of a MCP to "method" shim in a sandbox.
for long agent sessions, I would expect a very high cache hit rate unless you're editing the system prompt, tools, or history between turns, or some turns take longer than the cache timeout
I've seen folks say that the future of using computers will be with an LLM that generates code on the fly to accomplish tasks. I think this is a bit ridiculous, but I do think that operating computers through natural language instructions is superior for a lot of cases and that seems to be where we are headed.
I can see a future where software is built with a CLI interface underneath the (optional) GUI, letting an LLM hook directly into the underlying "business" logic to drive the application. Since LLM's are basically text machines, we just need somebody to invent a text-driven interface for them to use...oh wait!
Imagine booking a flight - the LLM connects to whatever booking software, pulls a list of commands, issues commands to the software, and then displays the output to the user in some fashion. It's basically just one big language translation task, something an LLM is best at, but you still have the guardrails of the CLI tool itself instead of having the LLM generate arbitrary code.
Another benefit is that the CLI output is introspectable. You can trace everything the LLM is doing if you want, as well as validate its commands if necessary (I want to check before it uses my credit card). You don't get this if it's generating a python script to hit some API.
Even before LLM's developers have been writing GUI applications as basically a CLI + GUI for testability, separation of concerns etc. Hopefully that will become more common.
Also this article was obviously AI generated. I'm not going to share my feelings about that.
Might need to iterate on them more because it's still quite obviously machine written, and a lot of people find it disrespectful to read content that was LLM generated.
The article says the LLM has to load 15540 tokens every time, I wonder if that can be reduced while retaining the context maybe with deduplications, removing superfluous words, using shorter expressions with the same meaning or things like that.
A lot of providers already have native CLI tools with usually better auth support and longer sessions than MCP as well as more data in their training set on how to use those cli tools for many things. So why convert mcp->cli tool instead of using the existing cli tools in the first place? Using the atlassian MCP is dog shit for example, but using acli is great. Same for github, aws, etc.
I know I saw something about the Next.js devs experimenting with just dumping an entire index of doc files into AGENTS.md and it being used significantly more by Claude than any skills/tool call stuff.
personal experience, definitely yes. You can try it out with `gh` rather than `Github MCP`. You'll see the difference immediately (espicially more if you have many MCPs)
I had deepseek explain MCP to me. Then I asked what was the point of persistent connections and it said it was pretty much hipster bullshit and that some url to post to is really enough for an llm to interact with things.
There is some important context missing from the article.
First, MCP tools are sent on every request. If you look at the notion MCP the search tool description is basically a mini tutorial. This is going right into the context window. Given that in most cases MCP tool loading is all or nothing (unless you pre-select the tools by some other means) MCP in general will bloat your context significantly. I think I counted about 20 tools in GitHub Copilot VSCode extension recently. That's a lot!
Second, MCP tools are not compossible. When I call the notion search tool I get a dump of whatever they decide to return which might be a lot. The model has no means to decide how much data to process. You normally get a JSON data dump with many token-unfriendly data-points like identifiers, urls, etc. The CLI-based approach on the other hand is scriptable. Coding assistant will typically pipe the tool in jq or tail to process the data chunk by chunk because this is how they are trained these days.
If you want to use MCP in your agent, you need to bring in the MCP model and all of its baggage which is a lot. You need to handle oauth, handle tool loading and selection, reloading, etc.
The simpler solution is to have a single MCP server handling all of the things at system level and then have a tiny CLI that can call into the tools.
In the case of mcpshim (which I posted in another comment) the CLI communicates with the sever via a very simple unix socket using simple json. In fact, it is so simple that you can create a bash client in 5 lines of code.
This method is practically universal because most AI agents these days know how to use SKILLs. So the goal is to have more CLI tools. But instead of writing CLI for every service you can simply pivot on top of their existing MCP.
This solves the context problem in a very elegant way in my opinion.
So basically the best way to use MCP is not to use it at all and just call the APIs directly or through a CLI. If those dont exist then wrapping the MCP into a CLI is the second best thing.
Makes you wonder whats the point of MCP
This was my initial understanding but if you want ai agents to do complex multi step workflows I.e. making data pipelines they just do so much better with MCP.
After I got the MCP working my case the performance difference was dramatic
I have never had a problem using cli tools intead of mcp. If you add a little list of the available tools to the context it's nearly the same thing, though with added benefits of e.g. being able to chain multiple together in one tool call
Or write your own MCP server and make lots of little tools that activate on demand or put smarts or a second layer LLM into crafting GQL queries on the fly and reducing the results on the fly. They're kinda trivial to write now.
I do agree that MCP context management should be better. Amazon kiro took a stab at that with powers
From your description, GraphQL or SQL could be a good solution for AI context as well.
SQL is peak for data retrieval (obviously) but challenging to deploy for multitenant applications where you can't just give the user controlled agent a DB connection. I found it every effective to create a mini paquet "data ponds" on the fly in s3 and allow the agent to query it with duckdb (can be via tool call but better via a code interpreter). Nice thing with this approach is you can add data from any source and the agent can join efficiently.
Is this article from a while back?
> Before your agent can do anything useful, it needs to know what tools are available. MCP’s answer is to dump the entire tool catalog into the conversation as JSON Schema. Every tool, every parameter, every option.
Because this simply isn't true anymore for the best clients, like Claude Code.
Similar to how Skills were designed[1] to be searchable without dumping everything into context, MCP tools can (and does in Claude Code) work the same way.
See https://www.anthropic.com/engineering/advanced-tool-use and https://x.com/trq212/status/2011523109871108570 and https://platform.claude.com/docs/en/agents-and-tools/tool-us...
[1] https://agentskills.io/specification#progressive-disclosure
FYI the blog has direct comparison to Anthropic’s Tool Search.
Regardless, most MCPs are dumping. I know Cloudflare MCP is amazing but other 1000 useful MCPs are not.
If we use prompt caching - isn't a largish MCP tools section just like a fixed token penalty in return for higher speed at runtime, because tools don't need to be discovered on demand, and that's the better tradeoff? At least for the most powerful models it doesn't feel like their quality goes down much with a few MCP servers. I might be missing something.
Does tool calling in general bloat context, or is there something particular about MCP?
One thing I have read recently is that when you make a tool call it forces the model to go back to the agent. The effect of this is that the agent then has to make another request with all of the prompt (include past messages), these will be "cached" tokens, but they're still expensive. So if you can amortize the tool calls by having the model either do many at once or chaining them with something like bash you'll be better off.
I suspect this might be why cursor likes writing bash scripts so much, simple shell commands are going to be very token heavy because of the frequency of interrupts.
MCP includes tool definitions in context, whereas models just "know" shell commands and common language tools.
This looks related to Awesome CLIs/TUIs and terminal trove which has lots both CLI and TUI apps.
Awesome TUIs: https://github.com/rothgar/awesome-tuis
Awesome CLIs: https://github.com/agarrharr/awesome-cli-apps
Terminal Trove: https://terminaltrove.com/
I guess this is another one shows that the CLI and Unix is coming back in 2026.
I actually want to combine this and CLIHub into a directory where someone can download all the official MCPs or CLIs (or MCP to CLIs) with a single command
After reading Cloudflare's Code Mode MCP blog post[1] I built CMCP[2] which lets you aggregate all MCP servers behind two mcp tools, search and execute.
I do understand anthropic's Tool Search helps with mcp bloat, but it's limited only to claude.
CMCP currently supports codex and claude but PRs are welcome to add more clients.
[1]https://blog.cloudflare.com/code-mode-mcp/ [2]https://github.com/assimelha/cmcp
did you check the token usage comparison between cmcp and cli?
Hehe... nice one. I think we are all thinking the same thing.
I've also launched https://mcpshim.dev (https://github.com/mcpshim/mcpshim).
The unix way is the best way.
Pretty sure I saw this one a couple of weeks back, or something very similar to it..
https://github.com/philschmid/mcp-cli
Edit: Turns out was https://github.com/steipete/mcporter noted elsewhere in the thread, but mcp-cli looks like a very similar thing.
Nice!
Compared both
---
TL;DR CLIHUB compiles MCP servers into portable, self-contained binaries — think of it like a compiler. Best for distribution, CI, and environments where you can't run a daemon.
mcpshim is a runtime bridge — think of it like a local proxy. Best for developers juggling many MCP servers locally, especially when paired with LLM agents that benefit from persistent connections and lightweight aliases.
---
https://cdn.zappy.app/b908e63a442179801e406b01cf412433.png (table comparison)
---
I was happy with playwright like MCPs that require the daemon so didn't convert them to CLIs.
My use cases are almost all 3rd party integrations.
Have you seen any improvements converting on MCPs that require persistency into CLI?
Nice. Love it.
One important aspect of mcpshim which you might want to bring into clihub is the history idea. Imagine if the model wants to know what it did couple of days ago. It will be nice to have an answer for that if you record the tool calls in a file and then allow the agent to query the file.
I'm looking at this from a slightly different level of abstraction.
The CLI approach definitely has practical benefits for token reduction. Not stuffing the entire schema into the runtime context is a clear win. But my main interest lies less in "token cost" and more in "how we structure the semantic space."
MCP is fundamentally a tool-level protocol. Existing paradigms like Skills already mitigate context bloat and selection overhead pretty well via tool discovery and progressive disclosure. So framing this purely as "MCP vs CLI" feels more like shifting the execution surface rather than a fundamental architectural shift.
The direction I'm exploring is a bit different. Instead of treating tools as the primary unit, what if we normalize the semantic primitives above them (e.g., "search," "read," "create")? Services would then just provide a projection of those semantics. This lets you compress the semantic space itself, expose it lazily, and only pull in the concrete tool/CLI/MCP adapters right at execution time.
You can arguably approximate this with Skills, but the current mental model is still heavily anchored to "tool descriptions"—it doesn't treat normalized semantics as first-class citizens. So while the CLI approach is an interesting optimization, I'm still on the fence about whether it's a real structural paradigm shift beyond just saving tokens.
Ultimately, shouldn't the core question be less about "how do we expose fewer tools," and more about "how do we layer and compress the semantic space the agent has to navigate?"
>what if we normalize the semantic primitives above them (e.g., "search," "read," "create")?
Trying to dictate the abstractions that should be used is not bitter lesson pilled.
ports & adapters :)
Haha I agree that my opinion is kind of that But more like ports & adapters for semantic space, not just IO boundaries.
If we can abstract the tools one layer further for ai, it might reduce the attention it needs to spend navigating them and leave more context window for actual reasoning
Related:
https://blog.cloudflare.com/code-mode-mcp/
https://news.ycombinator.com/item?id=47129241
True for coding agents running SotA models where you're the human-in-the-loop approving, less true for your deployed agents running on cheap models that you don't see what's being executed.
But yeah, a concrete example is playwright-mcp vs playwright-cli: https://testcollab.com/blog/playwright-cli
Probably oversold here because if you read the fine print, the savings only come in cases when you don't need the bytes in context.
That makes sense for some of the examples the described (e.g. a QA workflow asking the agent to take a screenshot and put it into a folder).
However, this is not true for an active dev workflow when you actually do want it to see that the elements are not lining up or are overlapping or not behaving correctly. So token savings are possible...if your use case doesn't require the bytes in context (which most active dev use cases probably do)*
This is cool!
I was actually thinking if I should support daemons just to support playwright. Now I don't have a use case for it
I feel like the permanent fix is for the AI labs to figure out better attention methods that increase context length without extra inference cost, plus deeper discounts (like -99%) for people being able to add system prompts to their accounts that are cached permanently.
This way you build all your MCPs into the system prompt, save the prompt to the AI provider, then use it without overpaying API costs.
The current "tools-on-demand" workarounds should be great for infrequent tools but the future will probably bring agents with dozens of tools that need them in context to flexibly many of them in the same context window. So we just need to make the context windows longer and make this capability cheaper to use.
Awesome stuff. I have a 'root' cli that i namespace stuff into so to remove the need to pass around paths, e.g: `./cli <cmd> ...`
I was just looking for a linear CLI earlier today. Awesome that the CLI converter uses that as an example. Nice!
Not just cheaper in terms of token usage but accuracy as well.
Even the smallest models are RL trained to use shell commands perfectly. Gemini 3 flash performs better with a cli with 20 commands vs 20+ tools in my testing.
cli also works well in terms of maintaining KV cache (changing tools mid say to improve model performance suffers from kv cache vs cli —help command only showing manual for specific command in append only fashion)
Writing your tools as unix like cli also has a nice benefit of model being able to pipe multiple commands together. In the case of browser, i wrote mini-browser which frontier models use much better than explicit tools to control browser because they can compose a giant command sequence to one shot task.
https://github.com/runablehq/mini-browser
I’m not sure how this works. A lot of that tool description is important to the Agent understanding what it can and can’t do with the specific MCP provider. You’d have to make up for that with a much longer overarching description. Especially for internal only tools that the LLM has no intrinsic context for.
I can give example.
LLM only know `linear` tool exists.
I ask "get me the comments in the last issue"
Next call LLM does is
`linear --help 2>&1 | grep -i -E "search|list.issue|get.issue")` then `linear list-issues --raw '{"limit": 3}' -o json 2>&1 | head -80)` then `linear list-comments --issue-id "abc1ceae-aaaa-bbbb-9aaa-6bef0325ebd0" 2>&1)`
So even the --help has filtering by default. Current models are pretty good
These days you can rewrite everything yourself for very cheap. So this is `mcporter` rewritten. I prefer to use Rust personally for rewrites. Opus 4.6 can churn it out pretty quickly if that's what you want. To be honest, almost all software that I want to try these days I don't even install. Instead I'd rather read the README and produce a personal version. This allows encoding idiosyncrasies and specifics that another author will not accept.
This sounds similar to MCPorter[0], can anyone point out the differences?
[0] https://github.com/steipete/mcporter
Main differences are
CLIHub
- written in go
- zero-dependency binaries
- cross-compilation built-in (works on all platforms)
- supports OAuth2 w/ PKCE, S2S, Google SA, API key, basic, bearer. Can be extended further
MCPorter
- TS
- huge dependency list
- runtime dependency on bun
- Auth supports OAuth + basic token
- Has many features like SDK, daemons (for certain MCPs), auto config discovery etc.
MCPorter is more complete tbh. Has many nice to have features for advanced use cases.
My use case is simple. Does it generate a CLI that works? Mainly oauth is the blocker since that logic needs to be custom implemented to the CLI.
I like this approach ... BUT the big win for me is audit logs. CLIs naturally leave a trail you can replay.
ALSO... the permission boundary is clearer. You can whitelist commands, flags, working dir... it becomes manageable.
HOWEVER... packaging still matters. A “small” CLI that pulls in a giant runtime kills the benefit.
I want the discipline of small protocol plus big cache. Cheap models can summarize what they did and avoid full context in every step...
Is there any redeeming quality of MCP vs a skill with CLI tool? Right now it looks like the latter is a clear winner.
Maybe MCP can help segregate auto-approve vs ask more cleanly, but I don't actually see that being done.
MCP defines a consistent authentication protocol. This is the real issue with CLIs, each CLI can (and will) have a different way of handling authentication (env variables, config set, JSON, yml, etc).
But tbh there's no reason agents can't abstract this out. As long as a CLI has a --help or similar (which 99% do) with a description of how to login, then it can figure it out for you. This does take context and tool calls though so not hugely efficient.
Why are they using JSON in the context? I thought we'd figured out that the extra syntax was a waste of tokens?
MCP has some schemas though. CLI is a bit of a mess.
But MCP today isn’t ideal. I think we need to have some catalogs where the agents can fetch more information about MCP services instead of filling the context with not relevant noise.
It's the same from functionality perspective. The schema's are converted to CLI versions of it. It's a UI change more than anything.
You are free to build tools that emit/ingest json, and provide a json schema upon request.
The point is push vs pull.
So much incorrect and misinformation in these comments. As someone who is building an agent[0] with MCP tools, neither the MCP tool description nor the response is the problem. Both of those are easily solved by not bloating them.
The real killer is the input tokens on each step. If you have 100k tokens in the conversation, and the LLM calls an MCP tool, the output and the existing conversation is sent back. So now you've input 200k tokens to the LLM.
Now imagine 10 tool calls per user message - or 50. You're sending 1-5M input tokens, not because the MCP definitions or tool responses are large, but because at each step, you have to send the whole conversation again.
"what about caching" - Only 90% savings, also cache misses are surprisingly common (we see as low as 40% cache hit rate)
"MCP definitions are still large" - not compared to any normal conversation. Also these get cached
We've seen the biggest savings by batching/parallelizing tool calls. I suspect the future of LLM tool usage will have a different architecture, but CLI doesn't solve the problems either.
[0] https://ziva.sh, it's an agent specialized for Godot[1]
[1] https://godotengine.org
But this is just the nature of LLMs (so far). Every "conversation" involves sending the entire conversation history back.
The article misses imo the main benefit of CLIs vs _current_ MCP implementations [1], the fact that they can be chained together with some sort of scripting by the agent.
Imagine you want to sum the total of say 150 order IDs (and the API behind the scenes only allows one ID per API calls).
With MCP the agent would have to do 150 tool calls and explode your context.
With CLIs the agent can write a for loop in whatever scripting language it needs, parse out the order value and sum, _in one tool call_. This would be maybe 500 tokens total, probably 1% of trying to do it with MCP.
[1] There is actually no reason that MCP couldn't be composed like this, the AI harnesses could provide a code execution environment with the MCPs exposed somehow. But noone does it ATM AFIAK. Sort of a MCP to "method" shim in a sandbox.
a 90% saving is huge isn't it?
for long agent sessions, I would expect a very high cache hit rate unless you're editing the system prompt, tools, or history between turns, or some turns take longer than the cache timeout
So it's more of a RAG via CLI than MCP.
I've seen folks say that the future of using computers will be with an LLM that generates code on the fly to accomplish tasks. I think this is a bit ridiculous, but I do think that operating computers through natural language instructions is superior for a lot of cases and that seems to be where we are headed.
I can see a future where software is built with a CLI interface underneath the (optional) GUI, letting an LLM hook directly into the underlying "business" logic to drive the application. Since LLM's are basically text machines, we just need somebody to invent a text-driven interface for them to use...oh wait!
Imagine booking a flight - the LLM connects to whatever booking software, pulls a list of commands, issues commands to the software, and then displays the output to the user in some fashion. It's basically just one big language translation task, something an LLM is best at, but you still have the guardrails of the CLI tool itself instead of having the LLM generate arbitrary code.
Another benefit is that the CLI output is introspectable. You can trace everything the LLM is doing if you want, as well as validate its commands if necessary (I want to check before it uses my credit card). You don't get this if it's generating a python script to hit some API.
Even before LLM's developers have been writing GUI applications as basically a CLI + GUI for testability, separation of concerns etc. Hopefully that will become more common.
Also this article was obviously AI generated. I'm not going to share my feelings about that.
Ofc it is written by ai, I have a skill for it -
https://github.com/thellimist/thellimist.github.io/blob/mast...
https://github.com/thellimist/thellimist.github.io/blob/mast...
I dump a voice message, then blog comes out. Then I modify a bunch of things, and iterate 1-2 hours to get it right
Might need to iterate on them more because it's still quite obviously machine written, and a lot of people find it disrespectful to read content that was LLM generated.
Can LLMs compress those documents into smaller files that still retain the full context?
What do you mean?
The article says the LLM has to load 15540 tokens every time, I wonder if that can be reduced while retaining the context maybe with deduplications, removing superfluous words, using shorter expressions with the same meaning or things like that.
You just reinvented Skills
I don't prefer to use online skills where half has malware
Official MCPs are trusted. Official MCPs CLIs are trusted.
Did he? Skills are for CLIs, not for converting MCPs into CLIs.
A lot of providers already have native CLI tools with usually better auth support and longer sessions than MCP as well as more data in their training set on how to use those cli tools for many things. So why convert mcp->cli tool instead of using the existing cli tools in the first place? Using the atlassian MCP is dog shit for example, but using acli is great. Same for github, aws, etc.
Cheaper, but is it more effective?
I know I saw something about the Next.js devs experimenting with just dumping an entire index of doc files into AGENTS.md and it being used significantly more by Claude than any skills/tool call stuff.
personal experience, definitely yes. You can try it out with `gh` rather than `Github MCP`. You'll see the difference immediately (espicially more if you have many MCPs)
The models are trained on gh though. Try with a lesser-known CLI.
I did - I have my almost a dozen CLIs that are custom built that I'm using. Very reliable.
It still needs to do discovery (--help etc.), always gets the job done
A very good example of this is playwright-cli vs Playwright MCP: https://github.com/microsoft/playwright-cli
The biggest difference is state, but that's also kind of easy from CLI, the tool just have to store it on disk, not in process memory.
I had deepseek explain MCP to me. Then I asked what was the point of persistent connections and it said it was pretty much hipster bullshit and that some url to post to is really enough for an llm to interact with things.
lol
The article's link to clihub.sh is broken. Looks like https://clihub.org/ is the correct link? I've added that to the toptext as well.
Edit: took out because I think that was something different.
Good catch.
I didn't release the website yet. I'll remove the link