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Tools: Code Is All You Need
- briandw 1y agoAnyone else switch their LLM subscription every month? I'm back on ChatGPT for O3 use, but expect that Grok4 will be next.
- mritchie712 1y ago> 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
- lsaferite 1y agoJust as a related aside, you could literally make that bottom section into a super simple stdio MCP Server and attach that to Claude Code. Each of your operations could be a tool and have a well-defined schema for parameters. Then you are giving the LLM a more structured and defined way to access your custom commands. I'm pretty positive there are even pre-made MCP Servers that are designed for just this activity. Edit: First result when looking for such an MCP Server: https://github.com/inercia/MCPShell https://github.com/inercia/MCPShell
- gbrindisi 1y agowouldn't this defeat the point? Claude Code already has access to the terminal, adding specific instruction in the context is enough
- lsaferite 1y agoNo. You are giving textual instructions to Claude in the hopes that it correctly generates a shell command for you vs giving it a tool definition with a clearly defined schema for parameters and your MCP Server is, presumably, enforcing adherence to those parameters BEFORE it hits your shell. You would be helping Claude in this case as you're giving a clearer set of constraints on operation.
- fassssst 1y agoEither way it is text instructions used to call a function (via a JSON object for MCP or a shell command for scripts). What works better depends on how the model you’re using was post trained and where in the prompt that info gets injected.
- wrs 1y agoWell, with MCP you’re giving textual instructions to Claude in hopes that it correctly generates a tool call for you. It’s not like tool calls have access to some secret deterministic mode of the LLM; it’s still just text. To an LLM there’s not much difference between the list of sample commands above and the list of tool commands it would get from an MCP server. JSON and GNU-style args are very similar in structure. And presumably the command is enforcing constraints even better than the MCP server would.
- lsaferite 1y agoNot strictly true. The LLM provider should be running a constrained token selection based off of the json schema of the tool call. That alone makes a massive difference as you're already discarding non-valid tokens during the completion at a low level. Now, if they had a BNF Grammer for each cli tool and enforced token selection based on that, you'd be much better off than unrestrained token selection.
- jayd16 1y agoI feel like I'm taking crazy pills sometimes. You have a file with a set of snippets and you prefer to ask the AI to hopefully run them instead of just running it yourself?
- lreeves 1y agoThe commands aren't the special sauce, it's the analytical capabilities of the LLM to view the outputs of all those commands and correlate data or whatever. You could accomplish the same by prefilling a gigantic context window with all the logs but when the commands are presented ahead of time the LLM can "decide" which one to run based on what it needs to do.
- light_hue_1 1y agoYes. I'm not the poster but I do something similar. Because now the capabilities of the model grow over time. And I can ask questions that involve a handful of those snippets. When we get to something new that requires some doing, it becomes another snippet. I can offload everything I used to know about an API and never have to think about it again.
- mritchie712 1y agothe snippets are examples. You can ask hundreds of variations of similar, but different, complex questions and the LLM can adjust the example for that need. I don't have a snippet for, "find all 500's for the meltano service for duckdb syntax errors", but it'd easily nail that given the existing examples.
- dingnuts 1y agobut if I know enough about the service to write examples, most of the time I will know the command I want, which is less typing, faster, costs less, and doesn't waste a ton of electricity. In the other cases I see what the computer outputs, LEARN, and then the functionality of finding what I need just isn't useful next time. Next time I just type the command. I don't get it.
- 1y ago
- chriswarbo 1y agoI use a similar file, but just for myself (I've never used an LLM "agent"). I live in Emacs, but this is the only thing I use org-mode for: it lets me fold/unfold the sections, and I can press C-c C-c over any of the code snippets to execute it. Some of them are shell code, some of them are Emacs Lisp code which generates shell code, etc.
- stpedgwdgfhgdd 1y agoI do something similar, but the problem is that claude.md keeps on growing. To tackle this, I converted a custom prompt into an application, but there is an interesting trade-off. The application is deterministic. It cannot deal with unknown situations. In contrast to CC, which is way slower, but can try alternative ways of dealing with an unknown situation. I ended up with adding an instruction to the custom command to run the application and fix the application code (TDD) if there is a problem. Self healing software… who ever thought
- e12e 1y agoYou're letting the LLM execute privileged API calls against your production/test/staging environment, just hoping it won't corrupt something, like truncate logs, files, databases etc? Or are you asking it to provide example commands that you can sanity check? I'd be curious to see some more concrete examples.
- tayloramurphy 1y agoFun to see Meltano mentioned here :)
- mritchie712 1y agomeltano4life
- mindwok 1y agoMore appropriately: the terminal is all you need. 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.
- jasonthorsness 1y agoI never knew how far it was possible to go in bash shell with the built-in tools until I saw the LLMs use them.
- zahlman 1y agoPossibly because most people who could mentor you, would give up and switch to their preference of {Perl, Python, Ruby, PHP, ...} far earlier. (Check out Dave Eddy, though. https://github.com/bahamas10 https://github.com/bahamas10 ; also occasionally streams on YouTube and then creates short educational video content there: https://www.youtube.com/@yousuckatprogramming https://www.youtube.com/@yousuckatprogramming )
- skydhash 1y agoSpurred by macos switch to zsh, I've gone all in on zsh customization (with oh-my-zsh). But since I've moved to Linux, it's been bash daily (with a brief fish interlude). Everything under the sun works with bash.
- pclowes 1y agoDirectionally 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.
- abdulhaq 1y agothis is a really good take
- foobarbecue 1y agoHype cycle for drones and VR was similar -- at the peak, you have people claiming drones will take over package delivery and everyone will spend their day in VR. Reality is that the applicability is more narrow.
- jangxx 1y agoI mean both of these things are actually happening (drone deliveries and people spending a lot of time in VR), just at a much much smaller scale than it was hyped up to be.
- giovannibonetti 1y ago
- graerg 1y ago> This is a significant advantage that an MCP (Multi-Component Pipeline) typically cannot offer Oh god please no, we must stop this initialism. We've gone too far.
- the_mitsuhiko 1y agoIt's the wrong acronym. I wrote this blog post on the bike and used an LLM to fix up the dictation that I did. While I did edit it heavily and rewrote a lot of things, I did not end up noticing that my LLM expanded MCP incorrectly. It's Model Context Protocol.
- apgwoz 1y agoAnd you shipped it to production. Just like real agentic coding! Nice!
- the_mitsuhiko 1y agoWhich I don't feel great about because I do not like to use LLMs for writing blog posts. I just really wanted to explore if I can write a blog post on my bike commute :)
- bitwize 1y agoWe're all in line to get de-rezzed by the MCP, one way or another.
- baal80spam 1y agoIsn't it a bit like saying: saw is all you need (for carpenters)? I mean, you _probably_ could make most furniture with only a saw, but why?
- nativeit 1y agoIn this analogy, do you have to design, construct, and learn from first principles to operate literally every other tool you'd like to use in addition to the saw?
- jasonthorsness 1y agoTools 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.
- blahgeek 1y ago> Code is expensive in terms of tokens and harder to constrain in environments It's also true for human. But then we invented functions / libraries / modules
- webdevver 1y agowhat does "MCP" stand for?
- dangus 1y agoI was about to say the same thing. It’s bad writing practice to do this, even if you are assuming your followers are following you. Especially for a site like Twitter that has a login wall.
- jasonlotito 1y agoI'm confused. It links to the definition in the first sentence, and I'm not sure what you mean by Twitter in this context.
- dangus 1y agoI could be wrong but I wonder if that was edited in. I could have sworn that wasn’t there before.
- empath75 1y agoIf you don't know what it is and can't be bothered to google it, then you probably aren't the audience for this.
- dangus 1y agoAssuming that people will google is terrible practice as a writer. Imagine writing a movie or a play where you want people to walk out of the theater and look something up. At best you are directing the audience away your work.
- komali2 1y agoMicrosoft Certified Professional, a very common certification. Oh wait... hm ;) perhaps the writing nerds had it right when they recommend always writing the full acronym out the first time it's used in an article, no matter how common one presumes it to be
- vidarh 1y agoI 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.
- empath75 1y agoThe problem with this is that you have to give your LLM basically unbounded access to everything you have access to, which is a recipe for pain.
- the_mitsuhiko 1y agoNot necessarily. I have a small little POC agentic tool on my side which is fully sandboxed, an it's inherently "non prompt injectable" by the data that it processes since it only ever passes that data through generated code. Disclaimer: it does not work well enough. But I think it shows great promise.
- elyase 1y agoThis 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 [1] https://huggingface.co/papers/2402.01030 https://huggingface.co/papers/2402.01030 [2] https://huggingface.co/papers/2401.00812 https://huggingface.co/papers/2401.00812 [3] https://huggingface.co/papers/2411.01747 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
- simonw 1y agoSomething 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. My assembly Mandelbrot experiment was the thing that made this click for me: https://simonwillison.net/2025/Jul/2/mandelbrot-in-x86-assembly-by-claude/ https://simonwillison.net/2025/Jul/2/mandelbrot-in-x86-assem...
- rasengan 1y agoMakes sense. I treat an LLM the same way I'd treat myself as it relates to context and goals when working with code. "If I need to do __________ what do I need to know/see?" I find that traditional tools, as per the OP, have become ever more powerful and useful in the age of LLMs (especially grep). Furthermore, LLMs are quite good at working with shell tools and functionalities (heredoc, grep, sed, etc.).
- dist-epoch 1y agoI've been using a VM for a sandbox, just to make sure it won't delete my files if it goes insane. With some host data directories mounted read only inside the VM. This creates some friction though. Feels like a tool which runs the AI agent in a VM, but then copies it's output to the host machine after some checks would help, so that it would feel that you are running it natively on the host.
- jitl 1y agoThis is very easy to do with Docker. Not sure it you want the vm layer as an extra security boundary, but even so you can just specify the VM’s docker api endpoint to spawn processes and copy files in/out from shell scripts.
- simonw 1y agoHave you tried giving the model a fresh checkout in a read-write volume?
- recursivedoubts 1y agoI would like to see MPC integrate the notion of hypermedia controls. Seems like that would be a potential way to get self-organizing integrations.
- FrustratedMonky 1y agoI wonder if having 2 LLM's communicate will eventually be more like humans talking. With all the same problems.
- CuriouslyC 1y agoI already have agents managing different repositories ask each other questions and make requests. It works pretty well for the most part.
- victorbjorklund 1y agoI 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.
- light_hue_1 1y agoThat's handled by the MCP server in the sense of it doesn't do authentication, etc. it provides a simplified view of the world. If that's what you wanted you could have designed that as your poorly documented internal API differently to begin with. There's zero advantage to MCP in the scenario you describe aside from convincing people that their original API is too hard to use.
- the_mitsuhiko 1y ago> Sure, you could dump all the documentation into context for code generation, but that often requires more context than interacting with an MCP tool. MCP works exactly that way: you dump documentation into the context. That's how the LLM knows how to call your tool. Even for custom stuff I noticed that giving the LLM things to work with that it knows (eg: python, javascript, bash) beats it using MCP tool calling, and in some ways it wastes less context. YMMV, but I found the limit of tools available to be <15 with sonnet4. That's a super low amount. Basically the official playwright MCP alone is enough to fully exhaust your available tool space.
- JyB 1y ago
- forrestthewoods 1y agoUnpopular 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.
- lsaferite 1y agoPython CAN be a "shell script" in this case though... Tool composition over stdio will get you very very far. That's what an interface "from the 80s" does for you 45 years later. That same stdio composability is easily pipe into/through any number of cli tools written in any number of languages, compiled and interpreted.
- forrestthewoods 1y agoComposing via stdio is so bloody terrible. Layers and layers of bullshit string parsing and encoding and decoding. Soooo many bugs. And utterly undebuggable. A truly miserable experience.
- zahlman 1y agoAnd now you also understand many of the limitations LLMs have.
- osigurdson 1y agoNobody likes coding in bash but everyone does it (a little) because it is everywhere.
- vasusen 1y agoI 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.
- shelajev 1y agoit's the latter: "you can actually start telling it to write a Playwright Python script instead and run that". and while running the code might faster, it's unclear whether that approach scales well. Sending an MCP tool command to click the button that says "X", is something a small local LLM can do. Writing complex code after parsing significant amount of HTML (for correct selectors for example) probably needs a managed model.
- jumploops 1y agoWe’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. Ad infinitum.
- JyB 1y ago> It demands too much context. 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$?
- the_mitsuhiko 1y ago> This is solved trivially by having default initial prompts. All majors tools like Claude Code or Gemini CLI have ways to set them up. That only makes it worse. The MCP tools available all add to the initial context. The more tools, the more of the context is populated by MCP tool definitions.
- JyB 1y agoDo you mean that some tools (MCP clients) pass all functions of all configured MCP servers in the initial prompt? If that's the case: I understand the knee-jerk reaction but if it works? Also what theoretically prevents altering the prompt chaining logic in these tools to only expose a condensed list of MCP servers, not their whole capabilities, and only inject details based on LLM outputs? Doesn't seem like an insurmountable problem.
- antirez 1y agoI 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.
- rapind 1y agoSo I run my own business (product), I code everything, and I use claude-code. I also wear all the other hats and so I'd be happy to let Claude handle all of the coding if / when it can. I can confirm we're certainly not there yet. It's definitely useful, but you have to read everything. I'm working in a type-safe functional compiled language too. I'd be scared to try this flow in a less "correctness enforced" language. That being said, I do find that it works well. It's not living up to the hype, but most of that hype was obvious nonsense. It continues to surprise me with it's grasp on concepts and is definitely saving me some time, and more importantly making some larger tasks more approachable since I can split my time better.
- galdre 1y agoMy 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.
- chamomeal 1y agoI was just going to comment about clojure-mcp!! It’s far and away the coolest use of mcp I’ve seen so far. It can straight up debug your code, eval individual expressions, document return types of functions. It’s amazing. It actually makes me think that languages with strong REPLs are a better for languages than those without. Seeing clojure-mcp do its thing is the most impressive AI feat I’ve seen since I saw GPT-3 in action for the first time
- e12e 1y agohttps://github.com/bhauman/clojure-mcp https://github.com/bhauman/clojure-mcp
- manaskarekar 1y agoOff topic: That font/layout/contrast on the page is very pleasing and inviting.
- khalic 1y agoHonestly, 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.
- pizzathyme 1y ago100% agreed - he mentions 3 barriers to using MCP over code: "cost, speed, and general reliability". But all 3 of these could change by 10-100x within a few years, if not months. Just recently OpenAI dropped the price of using o3 by 80% This is not an environment where you can establish a durable manifesto
- luckystarr 1y agoI 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.
- tristanz 1y agoYou can combine MCPs within composable LLM generated code if you put in a little work. At Continual (https://continual.ai 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.
- JyB 1y agoThat autocompounding aspect of constantly refining initial prompts with more and more knowledge is so interesting. Gut feeling says it’s something that will be “standardized” in some way, exactly like what MCP did.
- tristanz 1y agoYes, I think you could get quite far with a few tools like memory/todo list + code interpreter + script save/load. You could probably get a lot farther though if you RLVRed this similar to how o3 uses web search so effectively during it's thinking process.
- wrs 1y agoMCP 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.
- CharlieDigital 1y ago> So maybe we need to look at ways to find a better abstraction for what MCP is great at, and code generation. For that that we might need to build better sandboxes and maybe start looking at how we can expose APIs in ways that allow an agent to do some sort of fan out / fan in for inference. Effectively we want to do as much in generated code as we can, but then use the magic of LLMs after bulk code execution to judge what we did. Por que no los dos? I ended up writing an OSS MCP server that securely executes LLM generated JavaScript using a C# JS interpreter (Jint) and handing it a `fetch` analogue as well as `jsonpath-plus`. Also gave it a built-in secrets manager. Give it an objective and the LLM writes its own code and uses the tool iteratively to accomplish the task (as long as you can interact with it via a REST API). For well known APIs, it does a fine job generating REST API calls. You can pretty much do anything with this. https://github.com/CharlieDigital/runjs https://github.com/CharlieDigital/runjs
- pamelafox 1y agoRegarding the Playwright example: I had the same experience this week attempting to build an agent first by using the Playwright MCP server, realizing it was slow, token-inefficient, and flaky, and rewriting with direct Playwright calls. MCP servers might be fun to get an idea for what's possible, and good for one-off mashups, but API calls are generally more efficient and stable, when you know what you want. Here's the agent I ended up writing: https://github.com/pamelafox/personal-linkedin-agent https://github.com/pamelafox/personal-linkedin-agent Demo: https://www.youtube.com/live/ue8D7Hi4nGs https://www.youtube.com/live/ue8D7Hi4nGs
- arkmm 1y agoThis is cool! Also have found the Playwright MCP implementation to be overkill and think of it more as a reference to an opinionated subset of the Playwright API. LinkedIn has this reputation of being notorious about making it hard to build automations on top of, did you run into any roadblocks when building your personal LinkedIn agent?
- zahlman 1y ago... Ah, reading these as well as more carefully reading TFA, I understand now that there is an MCP based on Playwright, and that Playwright itself is not considered an example of something that accidentally is an MCP despite having been released all the way back in January 2020. ... But now I still feel far away from understanding what MCP really is. As in: * What specifically do I have to implement in order to create one? * Now that the concept exists, what are the implications as the author of, say, a traditional REST API? * Now that the concept exists, what new problems exist to solve?
- pramodbiligiri 1y agoWouldn't the sweet spot for MCP be where the LLM is able to do most of the heavy lifting on its own (outputting some kind of structured or unstructured output), but needs a bit of external/dynamic data that it can't do without? The list of MCP servers/tools it can use should nail that external lookup in a (mostly) deterministic way. This would work best if a human is the end consumer of this output, or will receive manual vetting eventually. I'm not sure I'd leave such a system running unsupervised in production ("the Automation at Scale" part mentioned by the OP).
- ramoz 1y agoYou don't solve the problem of being able to rely on the agent to call the MCP. Hooks into the agent's execution lifecycle seem more reliable for deterministic behavior and supervision.
- pramodbiligiri 1y agoI agree. In any large backend software running on a server, it's the LLM invocation which would be a call out to an external system, and with proper validation around the results. At which point, calling an "MCP Server" is also just your backend software invoking one more library/service based on inspecting some part of the response from the LLM. This doesn't take away from the utility of MCP when it comes Claude Desktop and the likes!
- jrm4 1y agoYup, I can't help but think that a lot of the bad thinking comes from trying to avoid the following fact: LLMs are only good where your output does not need to be precise and/or verifiably "perfect," which is kind of the opposite of how code has worked, or has tried to work, in the past. Right now I got it for: DRAFTS of prose things -- and the only real killer in my opinion, autotagging thousands of old bookmarks. But again, that's just to have cool stuff to go back and peruse, not something that must be correc.t
- never_inline 1y agoThe problem I see with MCP is very simple. It's using JSON as the format and that's nowhere as expressive as a programming language. Consider a python function signature list_containers(show_stopped: bool = False, name_pattern: Optional[str] = None, sort: Literal["size", "name", "started_at"] = "name"). It doesn't even need docs Now convert this to JSON schema which is 4x larger input already. And when generating output, the LLM will generate almost 2x more tokens too, because JSON. Easier to get confused. And consider that the flow of calling python functions and using their output to call other tools etc... is seen 1000x more times in their fine tuning data, whereas JSON tool calling flows are rare and practically only exist in instruction tuning phase. Then I am sure instruction tuning also contains even more complex code examples where model has to execute complex logic. Then theres the whole issue of composition. To my knowledge there's no way LLM can do this in one response. vehicle = call_func_1() if vehicle.type == "car": details = lookup_car(vehicle.reg_no) else if vehicle.type == "motorcycle": details = lookup_motorcycle(vehicle.reg_ni) How is JSON tool calling going to solve this?
- chrisweekly 1y agoGreat point. But "the" problem with MCP? IMVHO (Very humble, non-expert) the half-baked or missing security aspects are more fundamental. I'd love to hear updates about that from ppl who know what they're talking about.
- 8note 1y agothe reason to use the llm is that you dont know ahead of time that the vehicle type is only a car or motorcycle, and the llm will also figure out a way to detail bycicles and boats and airplanes, and to consider both left and right shoes separately. the llm cant just be given this function because its specialized to just the two options. you could have it do a feedback loop of rewriting the python script after running it, but whats the savings at tha point? youre wasting tokens talking about cars in python when you already know is a ski, and the llm could ask directly for the ski details without writing a script to do it in between
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- prairieroadent 1y agomakes sense and if realized then deno is in excellent position to be one of the leading if not the main sandbox runtime for agents
- keybored 1y agotl;dr of one of today’s AI posts: all you need is code generation It’s 2025 and this is the epitome of progress. On the positive side code generation can be solid if you also have/can generate easy-to-read validation or tests for the generated code. I mean that you can read, of course.
- LudwigNagasena 1y agoI hit the same roadblock with MCP. If you work with data, LLM becomes a very expensive pipe with an added risk of hallucinations. It’s better to simply connect it to a Python environment enriched with integrations you need.
- SatvikBeri 1y agoI use Julia at work, which benefits from long-running sessions, because it compiles functions the first time they run. So I wrote a very simple MCP that lets Claude Code send code to a persistent Julia kernel using Jupyter. It had a much bigger impact than I expected – not only does test code run much faster (and not time out), but Claude seems to be much more willing to just run functions from our codebase rather than do a bunch of bespoke bash stuff to try and make something work. It's anecdotal, but CCUsage says my token usage has dropped nearly 50% since I wrote the server. Of course, it didn't have to be MCP – I could have used some other method to get Claude to run code from my codebase more frequently. The broader point is that it's much easier to just add a useful function to my codebase than it is to write something bespoke for Claude.
- macleginn 1y ago"Claude seems to be much more willing to just run functions from our codebase rather than do a bunch of bespoke bash stuff to try and make something work" -- simply because it knows that there is a kernel it can send code to?
- alganet 1y agoIt's finally happening. The acceleration of the full AI disillusionment: - LLMs will do everything. - Shit, they won't. I'll do some traditional programming to put it on a leash. - More traditional programming. - Wait, this traditional programming thing is quite good. - I barely use LLMs now. - Man, that LLM stuff was a bad trip. See you all on the other side!
- meowface 1y agoYou very much misunderstood the article (or, more likely, didn't even open it) because it fit your increasingly fringe skeptic narrative.
- alganet 1y agoI don't get it. I misunderstood _because_ it fits my narrative? Or I didn't opened it _because_ it fits my narrative? I think I nailed it, and the article approach is representative of the "oh shit, it can't do it all" phase, which soon will be followed by "more traditional programming". These guys promised you an LLM that can do magic, from curing cancer to solve democracy, while delivering half-assed shit that doesn't stand scrutiny (even for the non-hype purposes it was designed for), and somehow I am the "fringe skeptic". If skeptics are on the fringe, then who is in charge of mainstream?
- sakesun 1y agoActually, this is the way human brain work. It's what we now know as system-1 (automatic, unconscious) and system-2 (effortful, conscious) described in Thinking Fast and Slow book.
- novoreorx 1y agoLooks familiar, it seems to share some ideas with this one: LLM function calls dont scale, code orchestration is simpler, more effective Source: https://jngiam.bearblog.dev/mcp-large-data/ https://jngiam.bearblog.dev/mcp-large-data/ HN: https://news.ycombinator.com/item?id=44053744 https://news.ycombinator.com/item?id=44053744
- aussieguy1234 1y agoIf it can be done on the command line , I'll just give the Agent permission to run commands. But if I want the Agent to do something that can't be done with commands i.e. go into Google Docs and organize all my documents, then an MVP server would make sense.
- 22c 1y agoOn the idea of replacing ones self with a shell script, I think there's nothing stopping people (and it should probably be encouraged) with replacing ones use of an LLM with an LLM generated "shell script". Using an LLM to count how many Rs are in the word strawberry is silly. Using it to write a script to reliably determine how many <LETTER> are in <WORD> is not so silly. The same goes for many repeated task you'd have an LLM naively perform. I think that is essentially what the article is getting at, but it's got very little to do with MCP. Perhaps the author has more familiarity with "slop" MCP tools than I do.
- ctrlp 1y agoI typically do this too rather than use MCP. Have the LLM write the tool, along with tests to get it working, then use the tool.
- 1vuio0pswjnm7 1y ago"Perhaps as a non-software engineer, code is out of reach." Perhaps. Perhaps not. I am a "non-software engineer" and I use shell scripts to automate tasks every day. I think there is actually too much code within reach. It gets in the way. Every day, more code. One reason I like the Bourne shell is that it does not change much. It just keeps working.