> try completing a GitHub task with the GitHub MCP, then repeat it with the gh CLI tool. You'll almost certainly find the latter uses context far more efficiently and you get to your intended results quicker.
This is spot on. I have a "devops" folder with a CLAUDE.md with bash commands for common tasks (e.g. find prod / staging logs with this integration ID).
When I complete a novel task (e.g. count all the rows that were synced from stripe to duckdb) I tell Claude to update CLAUDE.md with the example. The next time I ask a similar question, Claude one-shots it.
This is the first few lines of the CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Purpose
This devops folder is dedicated to Google Cloud Platform (GCP) operations, focusing on:
- Google Cloud Composer (Airflow) DAG management and monitoring
- Google Cloud Logging queries and analysis
- Kubernetes cluster management (GKE)
- Cloud Run service debugging
## Common DevOps Commands
### Google Cloud Composer
```bash
# View Composer environment details
gcloud composer environments describe meltano --location us-central1 --project definite-some-id
# List DAGs in the environment
gcloud composer environments storage dags list --environment meltano --location us-central1 --project definite-some-id
# View DAG runs
gcloud composer environments run meltano --location us-central1 dags list
# Check Airflow logs
gcloud logging read 'resource.type="cloud_composer_environment" AND resource.labels.environment_name="meltano"' --project definite-some-id --limit 50
I have used MCP daily for a few months. I'm now down to a single MCP server: terminal (iTerm2). I have OpenAPI specs on hand if I ever need to provide them, but honestly shell commands and curl get you pretty damn far.
Directionally I think this is right. Most LLM usage at scale tends to be filling the gaps between two hardened interfaces. The reliability comes not from the LLM inference and generation but the interfaces themselves only allowing certain configuration to work with them.
LLM output is often coerced back into something more deterministic such as types, or DB primary keys. The value of the LLM is determined by how well your existing code and tools model the data, logic, and actions of your domain.
In some ways I view LLMs today a bit like 3D printers, both in terms of hype and in terms of utility. They excel at quickly connecting parts similar to rapid prototyping with 3d printing parts. For reliability and scale you want either the LLM or an engineer to replace the printed/inferred connector with something durable and deterministic (metal/code) that is cheap and fast to run at scale.
Additionally, there was a minute during the 3D printer Gardner hype cycle where there were notions that we would all just print substantial amounts of consumer goods when the reality is the high utility use case are much more narrow. There is a corollary here to LLM usage. While LLMs are extremely useful we cannot rely on LLMs to generate or infer our entire operational reality or even engage meaningfully with it without some sort of pre-existing digital modeling as an anchor.
Tools are constraints and time/token savers. Code is expensive in terms of tokens and harder to constrain in environments that can’t be fully locked-down because network access for example is needed by the task. You need code AND tools.
I frankly use tools mostly as an auth layer for things were raw access is too big a footgun without a permissions step. So I give the agent the choice of asking for permission to do things via the shell, or going nuts without user-interaction via a tool that enforces reasonable limitations.
Otherwise you can e.g just give it a folder of preapproved scripts to run and explain usage in a prompt.
This is similar to the tool call (fixed code & dynamic params) vs code generation (dynamic code & dynamic params) discussion: tools offer contrains and save tokens, code gives you flexibility. Some papers suggest that generating code is often superior and this will likely become even more true as language models improve
I am working on a model that goes a step beyond and even makes the distinction between thinking and code execution unnecessary (it is all computation in the end), unfortunately no link to share yet
Something I've realized about LLM tool use is that it means that if you can reduce a problem to something that can be solved by an LLM in a sandbox using tools in a loop, you can brute force that problem.
The job then becomes identifying those problems and figuring out how to configure a sandbox for them, what tools to provide and how to define the success criteria for the model.
That still takes significant skill and experience, but it's at a higher level than chewing through that problem using trial and error by hand.
I think the GitHub CLI example isn't entirely fair to MCP. Yes, GitHub's CLI is extensively documented online, so of course LLMs will excel at generating code for well-known tools. But MCP shines in different scenarios.
Consider internal company tools or niche APIs with minimal online documentation. Sure, you could dump all the documentation into context for code generation, but that often requires more context than interacting with an MCP tool. More importantly, generated code for unfamiliar APIs is prone to errors so you'd need robust testing and retry mechanisms built in to the process.
With MCP, if the tools are properly designed and receive correct inputs, they work reliably. The LLM doesn't need to figure out API intricacies, authentication flows, or handle edge cases - that's already handled by the MCP server.
So I agree MCP for GitHub is probably overkill but there are many legitimate use cases where pre-built MCP tools make more sense than asking an LLM to reverse-engineer poorly documented or proprietary systems from scratch.
Unpopular Opinion: I hate Bash. Hate it. And hate the ecosystem of Unix CLIs that are from the 80s and have the most obtuse, inscrutable APIs ever designed. Also this ecosystem doesn’t work on Windows — which, as a game dev, is my primary environment. And no, WSL does not count.
I don’t think the world needs yet another shell scripting language. They’re all pretty mediocre at best. But maybe this is an opportunity to do something interesting.
Python environment is a clusterfuck. Which UV is rapidly bringing into something somewhat sane. Python isn’t the ultimate language. But I’d definitely be more interested in “replace yourself with a UV Python script” over “replace yourself with a shell script”. Would be nice to see use this as an opportunity to do better than Bash.
I realize this is unpopular. But unpopular doesn’t mean wrong.
I think the Playwright MCP is a really good example of the overall problem that the author brings up.
However, I couldn’t really understand if he’s saying that the Playwright MCP is good to use for your own app or whether he means for your own app just tell the LLM directly to export Playwright code.
We’re playing an endless cat and mouse game of capabilities between old and new right now.
Claude Code shows that the models can excel at using “old” programmatic interfaces (CLIs) to do Real Work™.
MCP is a way to dynamically provide “new” programmatic interfaces to the models.
At some point this will start to converge, or at least appear to do so, as the majority of tools a model needs will be in its pre-training set.
Then we’ll argue about MPPP (model pre-training protocol pipeline), and how to reduce knowledge pollution of all the LLM-generated tools we’re passing to the model.
Eventually we’ll publish the Merrium-Webster Model Tool Dictionary (MWMTD), surfacing all of the approved tools hidden in the pre-training set.
Then the kids will come up with Model Context Slang (MCS), in an attempt to use the models to dynamically choose unapproved tools, for much fun and enjoyment.
This is solved trivially by having default initial prompts.
All majors tools like Claude Code or Gemini CLI have ways to set them up.
> You pass all your tools to an LLM and ask it to filter it down based on the task at hand. So far, there hasn't been much better approaches proposed.
Why is a "better" approach needed? If modern LLMs can properly figure it out? It's not like LLMs don't keep getting better with larger and larger context length.
I never had a problem with an LLM struggling to use the appropriate MCP function on it's own.
> But you run into three problems: cost, speed, and general reliability
- cost: They keep getting cheaper and cheaper. It's ridiculously inexpensive for what those tools provide.
- speed: That seem extremely short sighted. No one is sitting idle looking at Claude Code in their terminal. And you can have more than one working on unrelated topics. That defeats the purpose. No matter how long it takes the time spent is purely bonus. You don't have to spend time in the loop when asking well defined tasks.
- reliability: Seem very prompt correlated ATM. I guess some people don't know what to ask which is the main issue.
Having LLMS being able to complete tedious tasks involving so many external tools at once is simply amazing thanks to MCP. Anecdotal but just today it did a task flawlessly involving: Notion pages, Linear Ticket, git, GitHub PR, GitHub CI logs.
Being in the loop was just submitting one review on the PR. All the while I was busy doing something else. And for what, ~1$?
I have the feeling that's not really MCP specifically VS other ways, but it is pretty simply: at the current state of AI, to have a human in the loop is much better. LLMs are great at certain tasks but they often get trapped into local minima, if you do the back and forth via the web interface of LLMs, ask it to write a program, look at it, provide hints to improve it, test it, ..., you get much better results and you don't cut yourself out to find a 10k lines of code mess that could be 400 lines of clear code. That's the current state of affairs, but of course many will try very hard to replace programmers that is currently not possible. What it is possible is to accelerate the work of a programmer several times (but they must be good both at programming and LLM usage), or take a smart person that has a relatively low skill in some technology, and thanks to LLM make this person productive in this field without the long training otherwise needed. And many other things. But "agentic coding" right now does not work well. This will change, but right now the real gain is to use the LLM as a colleague.
It is not MCP: it is autonomous agents that don't get feedbacks from smart humans.
My absolute favorite use of MCP so far is Bruce Hauman's clojure-mcp. In short, it gives the LLM (a) a bash tool, (b) a persistent Clojure REPL, and (c) structural editing tools.
The effect is that it's far more efficient at editing Clojure code than any purely string-diff-based approach, and if you write a good test suite it can rapidly iterate back and forth just editing files, reloading them, and then re-running the test suite at the REPL -- just like I would. It's pretty incredible to watch.
Honestly, I’m getting tired of these sweeping statements about what developers are supposed to be, how it’s “the right way to use AI”. We are in uncharted territories that are changing by the day. Maybe we have to drop the self-assurance and opinionated view points and tackle this like a scientific problem.
I always dreamed of a tool which would know the intent, semantic and constraints of all inputs and outputs of any piece of code and thus could combine these code pieces automatically. It was always a fuzzy idea in my head, but this piece now made it a bit more clear. While LLMs could generate those adapters between distinct pieces automatically, it's a expensive (latency, tokens) process. Having a system with which not only to type the variables, but also to type the types (intents, semantic meaning, etc.) would be helpful but likely not sufficient. There has been so much work on ontologies, semantic networks, logical inference, etc. but all of it is spread all over the place. I'd like to have something like this integrated into a programming language and see what it feels like.
You can combine MCPs within composable LLM generated code if you put in a little work. At Continual (https://continual.ai), we have many workflows that require bulk actions, e.g. iterating over all issues, files, customers, etc. We inject MCP tools into a sandboxed code interpreter and have the agent generate both direct MCP tool calls and composable scripts that leverage MCP tools depending on the task complexity. After a bunch of work it actually works quite well. We are also experimenting with continual learning via a Voyager like approach where the LLM can save tool scripts for future use, allowing lifelong learning for repeated workflows.
MCP is literally the same as giving an LLM a set of man page summaries and a very limited shell over HTTP. It’s just in a different syntax (JSON instead of man macros and CLI args).
It would be better for MCP to deliver function definitions and let the LLM write little scripts in a simple language.
44 comments
[ 1.00 ms ] story [ 38.5 ms ] threadThis is spot on. I have a "devops" folder with a CLAUDE.md with bash commands for common tasks (e.g. find prod / staging logs with this integration ID).
When I complete a novel task (e.g. count all the rows that were synced from stripe to duckdb) I tell Claude to update CLAUDE.md with the example. The next time I ask a similar question, Claude one-shots it.
This is the first few lines of the CLAUDE.md
I have used MCP daily for a few months. I'm now down to a single MCP server: terminal (iTerm2). I have OpenAPI specs on hand if I ever need to provide them, but honestly shell commands and curl get you pretty damn far.
LLM output is often coerced back into something more deterministic such as types, or DB primary keys. The value of the LLM is determined by how well your existing code and tools model the data, logic, and actions of your domain.
In some ways I view LLMs today a bit like 3D printers, both in terms of hype and in terms of utility. They excel at quickly connecting parts similar to rapid prototyping with 3d printing parts. For reliability and scale you want either the LLM or an engineer to replace the printed/inferred connector with something durable and deterministic (metal/code) that is cheap and fast to run at scale.
Additionally, there was a minute during the 3D printer Gardner hype cycle where there were notions that we would all just print substantial amounts of consumer goods when the reality is the high utility use case are much more narrow. There is a corollary here to LLM usage. While LLMs are extremely useful we cannot rely on LLMs to generate or infer our entire operational reality or even engage meaningfully with it without some sort of pre-existing digital modeling as an anchor.
Oh god please no, we must stop this initialism. We've gone too far.
I mean, you _probably_ could make most furniture with only a saw, but why?
Otherwise you can e.g just give it a folder of preapproved scripts to run and explain usage in a prompt.
[1] https://huggingface.co/papers/2402.01030
[2] https://huggingface.co/papers/2401.00812
[3] https://huggingface.co/papers/2411.01747
I am working on a model that goes a step beyond and even makes the distinction between thinking and code execution unnecessary (it is all computation in the end), unfortunately no link to share yet
The job then becomes identifying those problems and figuring out how to configure a sandbox for them, what tools to provide and how to define the success criteria for the model.
That still takes significant skill and experience, but it's at a higher level than chewing through that problem using trial and error by hand.
My assembly Mandelbrot experiment was the thing that made this click for me: https://simonwillison.net/2025/Jul/2/mandelbrot-in-x86-assem...
Consider internal company tools or niche APIs with minimal online documentation. Sure, you could dump all the documentation into context for code generation, but that often requires more context than interacting with an MCP tool. More importantly, generated code for unfamiliar APIs is prone to errors so you'd need robust testing and retry mechanisms built in to the process.
With MCP, if the tools are properly designed and receive correct inputs, they work reliably. The LLM doesn't need to figure out API intricacies, authentication flows, or handle edge cases - that's already handled by the MCP server.
So I agree MCP for GitHub is probably overkill but there are many legitimate use cases where pre-built MCP tools make more sense than asking an LLM to reverse-engineer poorly documented or proprietary systems from scratch.
I don’t think the world needs yet another shell scripting language. They’re all pretty mediocre at best. But maybe this is an opportunity to do something interesting.
Python environment is a clusterfuck. Which UV is rapidly bringing into something somewhat sane. Python isn’t the ultimate language. But I’d definitely be more interested in “replace yourself with a UV Python script” over “replace yourself with a shell script”. Would be nice to see use this as an opportunity to do better than Bash.
I realize this is unpopular. But unpopular doesn’t mean wrong.
Seems like that would be a potential way to get self-organizing integrations.
However, I couldn’t really understand if he’s saying that the Playwright MCP is good to use for your own app or whether he means for your own app just tell the LLM directly to export Playwright code.
Claude Code shows that the models can excel at using “old” programmatic interfaces (CLIs) to do Real Work™.
MCP is a way to dynamically provide “new” programmatic interfaces to the models.
At some point this will start to converge, or at least appear to do so, as the majority of tools a model needs will be in its pre-training set.
Then we’ll argue about MPPP (model pre-training protocol pipeline), and how to reduce knowledge pollution of all the LLM-generated tools we’re passing to the model.
Eventually we’ll publish the Merrium-Webster Model Tool Dictionary (MWMTD), surfacing all of the approved tools hidden in the pre-training set.
Then the kids will come up with Model Context Slang (MCS), in an attempt to use the models to dynamically choose unapproved tools, for much fun and enjoyment.
Ad infinitum.
This is solved trivially by having default initial prompts. All majors tools like Claude Code or Gemini CLI have ways to set them up.
> You pass all your tools to an LLM and ask it to filter it down based on the task at hand. So far, there hasn't been much better approaches proposed.
Why is a "better" approach needed? If modern LLMs can properly figure it out? It's not like LLMs don't keep getting better with larger and larger context length. I never had a problem with an LLM struggling to use the appropriate MCP function on it's own.
> But you run into three problems: cost, speed, and general reliability
- cost: They keep getting cheaper and cheaper. It's ridiculously inexpensive for what those tools provide.
- speed: That seem extremely short sighted. No one is sitting idle looking at Claude Code in their terminal. And you can have more than one working on unrelated topics. That defeats the purpose. No matter how long it takes the time spent is purely bonus. You don't have to spend time in the loop when asking well defined tasks.
- reliability: Seem very prompt correlated ATM. I guess some people don't know what to ask which is the main issue.
Having LLMS being able to complete tedious tasks involving so many external tools at once is simply amazing thanks to MCP. Anecdotal but just today it did a task flawlessly involving: Notion pages, Linear Ticket, git, GitHub PR, GitHub CI logs. Being in the loop was just submitting one review on the PR. All the while I was busy doing something else. And for what, ~1$?
It is not MCP: it is autonomous agents that don't get feedbacks from smart humans.
The effect is that it's far more efficient at editing Clojure code than any purely string-diff-based approach, and if you write a good test suite it can rapidly iterate back and forth just editing files, reloading them, and then re-running the test suite at the REPL -- just like I would. It's pretty incredible to watch.
It would be better for MCP to deliver function definitions and let the LLM write little scripts in a simple language.