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Clojure MCP

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202 pointstodsacerdoti50 comments

Until you've tried using an LLM assistant fully hooked into a stateful REPL, you can't speculate. The experience is fantastic as the feedback for the code being developed is earlier and tighter.

The LLM agent will often write the code for a function and immediately follow that code up with several smoke testing expressions and the eval the whole thing in one go, function and tests.

It will creatively setup test harnesses to enable it to exercise code while it's being developed. (think html endpoints, starting and stopping servers, mocking)

And it goes on from there. Its an experience, and I submit to the reader that they experience it sooner than later bc its an extremely effective workflow and its AWESOME!

Is there a possibility that you could do a short video with a demonstration of your workflow using AI with REPL in Clojure?

flappy bird demo but for clojure mcp!

You might see this and think, "great, another hyped up vibe coding tool". If Clojure is about simplicity and understanding your code deeply (with the end goals of long-term maintenance and reliability), why would you need this?

When working with Clojure, I've been using LLMs primarily for two use cases:

    1. Search
    2. Design feedback

The first case is obvious to anyone who's used an LLM chat interface: it's often easier to ask an LLM for the answer than a traditional search engine.

The second case is more interesting. I believe the design of a system is more important than the language being used. I'd rather inherit a well-designed codebase in some other language over a poorly designed Clojure codebase any day. Due to the values of Clojure embedded in the language itself and the community that surrounds it, Clojure programmers are naturally encouraged to think first, code second.

The problem I've run into with the second case is that it often takes too much effort for me to get the context into the LLM for it to answer my questions in detail. As a result, I tend to reach for LLMs when I have a general design question that I can then translate into Clojure. Asking it specific questions about my existing Clojure code has felt like more effort than it's worth, so I've actually trained myself to make things more generic when I talk to the LLM.

This MCP with Claude Code seems like the tipping point where I can start asking questions about my code, not just asking for general design feedback. I hooked this up to a project of mine where I recently added multi-tenancy support (via an :app-id key), which required low-level changes across the codebase. I asked the following question with Claude Code and the Clojure MCP linked here:

> given that :app-id is required after setup, are there any places where :app-id should be checked that is missing?

It actually gave me some good feedback on specific files and locations in my code for about 10 seconds of effort. That said, it also cost me $0.48. This might be the thing that gets me to subscribe to a Claude Max plan...

This right here. There is a large segment of developers that have not hooked up indexed code, eg: "where I can start asking questions about my code". They treat it like a search engine. What you want is the LLM to INDEX your code. Then you can see the real power of LLMs. Spoiler alert: it is amazing.

And to make people get the point: indexing in this case would be to store it in a vector database and make the LLM be able to query it on demand.

It’s really powerful.

or even better (esp with highly expressive languages): just slurp the whole codebase, no vector db needed

I think you need to try the 3rd way:

    3. Agentic Coding (aka Vibe Coding)

This is what clojure-mcp with Claude Desktop lets you try. Or you can try Amazon Q CLI (there is a free tier https://aws.amazon.com/q/developer/pricing/). Not Clojure specific.

You need to find a workflow to leverage it. There are two approaches.

    1. Developer Guided

Here you setup the project and basic project structure. Add the dependencies you want to use, setup your src and test folders, and so on.

Then you start creating the namespaces you want, but you don't implement them, just create the `(ns ...)` with a doc-string that describes it. You can also start adding the public functions you want for it's API. Don't implement those either. Just add a signature and doc-string.

Then you create the test namespace for it. Creates a deftest for the functions you want to test, and add `(testing ...)` but don't add the body, just write the test description.

Now you tell the AI to fill in the implementation of the tests and namespace so that all described test cases pass and to run the test and iterate until it all does.

Then ask the AI to code review itself, and iterate on the code until it has no more comments.

Mention security, exception handling, logging, and so on as you see fit, if you explicitly call those concerns it'll work on them.

Rinse and repeat. You can add your own tests to be more sure, and also test things out and ask it to fix.

    2. Product Guided

Here you pretend to be the Product Manager. You create a project and start adding markdown files in it that describe the user stories, the features of the app/service and so on.

Then you ask AI to generate a design specification. You review that, and have it iterate on it until you like it.

Then you ask AI to break down a delivery plan, and a test plan to implement it. Review and iterate until you like it.

Then you ask AI to break up the delivery in milestones, and to create a break down of tasks for the first milestone. Review and iterate here.

Then you ask AI to implement the first task, with tests. Review and iterate. Then the next, and so on.

I'm skeptical because I don't think generating the Clojure code is the hard part. These ideas seem more like wishful thinking than actual productivity improvements with the current state of tech.

Developer guided: For the projects I'm currently working on, the understanding is the most difficult part, and writing the code is a way for me to check my understanding as I go. I do use LLMs to generate code when I feel like it can save me time, such as setting up a new project or scaffolding tests, but I think there are diminishing returns the larger and/or more complex the project is. Furthermore, I work on code that other people (or LLMs) are meant to understand, so I value code that is consistent and concise.

Product guided: Even with meat-based agents (i.e humans), there's a limit to how many Jira tickets I can write and junior engineers I can babysit, and this is one of the worst parts of the job to begin with. Furthermore, junior engineers often make mistakes which means I need to have my own understanding to fix the issues. That said, getting feedback from experienced colleagues is invaluable, and that's what I'm currently simulating with LLMs.

The point in the readme about using this with Claude Desktop to avoid API charges is a top tip for any coding-through-MCP setup with Claude.

I’m not a Clojure user but I’ve set up the Jetbrains MCP server and Claude Desktop has been working really well with it. I added a system prompt telling Claude to inspect the CLAUDE.md (used with Claude Code normally) whenever using Jetbrains to get project context, which is also working out really well.

but claude code also let's you run with a max subscription (not free but capped and with high usage, much cheaper than pure api costs)

yes it does, and I think that’s a great development - the flat rate access to Claude Code - but I’m only on the Pro subscription.

REPL-driven development seems like a natural fit with current agent concepts

nb: Bruce also did figwheel, a hot/live reloading tool for Clojurescript

https://figwheel.org

I'm no sure, if you mean having the LLM interact with the REPL. REPL requires a lot of discipline from the developer to keep track of its state. LLMs seem to be far worse at this sort of long-term state tracking than most humans. It would likely keep forgetting things, calling undefined functions, or mistaking its own interfaces.

I agree, it’s tricky, but agents are also great at inspecting stuff

right now the advice in the readme makes a lot of sense: use git (or similar) for checkpointing

a bit like climbing with ropes and anchors

> REPL requires a lot of discipline from the developer to keep track of its state

More discipline than what is required when trying to keep track of all of that in your head? Personally I use a REPL because I'm lazy and dumb, and the REPL helps me keep track and makes sure I don't do stupid mistakes. If I wanna know what value a thing has, I just ask the REPL, instead of trying to retrace some logic by reading the code again.

What I don't like about building systems at the REPL is that when I investigate a possibility and it works out, I have to identify the code that's actually needed, clean it up, and turn my ad-hoc tests in the REPL into unit tests. There's a trade-off between quick experimentation and the mess I create in search of immediate feedback.

I think an LLM could help with that. Ideally, I could say, "Please add the widget-muckle-frobnicate function from the current REPL session to the codebase. Fix or flag any shortcuts I took in its implementation, such as hardcoding configuration and naming functions 'foo' or 'bar', and add unit tests that include the examples I tried in the REPL as well as property-based tests to exercise edge cases."

> Please add the widget-muckle-frobnicate function from the current REPL session to the codebase.

I think this is your problem, those two things should be the same, not two different things. When I'm working with a Clojure REPL, I write down code in a .clj file, then evaluate that code via the REPL but without leaving my editor. "Moving" something from a REPL session to the codebase is a matter of saving the current file you're editing.

I wrote more about the exact process I typically use when going from "prototyping with a repl, to having a test case" (https://news.ycombinator.com/item?id=44106445) and it's essentially changing "comment" to "deftest" then saving the file again, now it's a unit test.

I do find value from LLMs in general, but I don't see much value in taking a process that takes a minute or less and trying to make LLMs do it.

Exactly (on the first two paragraphs). I (almost) never type anything into the REPL window itself.

I might type a "5" and evaluate it just to see if the REPL is busy or not, but otherwise, everything the REPL evaluates comes from an emacs source code buffer, sent via CIDER to the REPL. At most, it might be in a "Rich comment" and thus not evaluated next time I load that file, but those tend to be small, interactive tests and not defns.

Unless emacs crashes with the buffer unsaved, there's nothing the REPL has that the source file doesn't.

I usually have a buffer that is kind of like a Jupyter notebook, and it accumulates a lot of stuff that may or may not turn out to be useful in the end. If I'm doing the work to keep things tidy as I go, so it's close to production ready at the end, then it isn't much different from traditional programming. It ends up just being TDD in one file instead of two.

REPL requires less discipline if you combine it with TDD. If there's an "abandon ship, tear down and set up the right state from scratch" red button it helps.

I would like to see how to easily get regression tests from REPL driven development.

In general I wonder how to quickly set up mocks and such with chatbot integrations.

> how to easily get regression tests from REPL driven development

Usually it's a matter of renaming "comment" to "deftest", and move some things around. Not sure how/why it would be different when using a LLM compared to doing it manually.

There is often a misunderstanding that “REPL driven development” means typing things into the REPL prompt interactively and accumulating state that’s not easy to reproduce. Writing code down in comment blocks as you go is the solution to that, as you mentioned. With that technique I have no fear of restarting my REPL and getting back to where I was.

That's true. To be fair, if you come from a language like Ruby, JavaScript or Python, a REPL is basically a standalone experience, so I understand why people initially think it's like that, the name is more or less the same :) "Interactive Development" is maybe a better overall term that can lead to people thinking "Oh, what is that?" rather than "I've tried that already, what's the point?"

I like that. So you're saying that the switch between REPL and writing a test is much more seamless in a clojure system. That makes a lot of sense to me.

At a basic level any Lisp-like with an editor integrated REPL lets you put your cursor over the `greet` invocation below and evaluate it, and have the results shown in the manner of your choice (IDE window, inline temporary comment, etc).

This is all in the file as saved to your version control:

  (defn greet [name] 
    (-> (str "Hello, " name "!")
         println))

  (comment
    (greet "worldsayshi"))

It's 90% of the way to being a test already, just from the typical workflow.

But if you're maintaining a bunch of code blocks by comment toggling, why not just turn it into tests from the get go? I suppose there's a somewhat fine line between TDD and REPL given the right tooling for it.

Different purposes. I usually start out with comment blocks, for experimenting/prototyping, once happy, cement whatever assertions I did there, but inside deftests. Not everything is moved to a test though, some things are just "helpers" sort of, and those tend to remain inside comment blocks instead of being converted to deftest or similar.

I use the REPL as lightweight form of testing while I'm in the flow state, which is complementary to TDD. If there's too much code in the comment block, that's a good signal to move it into a formal test like you're saying, or at least move the code into a reusable function so the comment block can be shorter and focused on usage examples instead of implementation details.

Exactly. I start in the repl, but when it becomes a few expressions deep, I transfer it to the editor and turn it into a proper defun. Start 'sending' the defun to the repl and test it out in repl. Rince repeat.

> I start in the repl [...] I transfer it to the editor

That's exactly the kind of misunderstanding parent was talking about :)

Why start somewhere else than your editor, if it's already hooked up to the REPL? When I launch my REPL, I don't think it even accepts stdin, because there is no reason it has to.

There is no hard and fast rule (for me).

It is easy to start creating objects in the repl (say if you are testing an API) and work with those created objects in the repl, one step at a time. You are able to observe the behaviour of the objects every step of the way. This gives you a much better idea when you are writing the functions (usually copy-pasting from repl) in the editor.

I am sure it can be done from the editor itself. (say using the 'comment' block in clojure). It is just matter of preference.

> LLM compared to doing it manually.

When testing against an LLM you probably want to mock its responses for tests. Which is quite doable by copy-pasting its responses. I'm just curious what the "best" workflow is.

If I'd be using Clojure for this, it would look something like this:

1. Write a function that returns a LLM response

2. Play around with this function in a `comment` block, highlighting what I'm testing

3. If I find a response I'd like to "cement"/save and use for testing, Conjure (what I use to connect to the REPL) has a command that is basically "evaluate what's highlighted in the editor with whatever that returns when evaluated", so that basically copy-pastes the return value into the editor

4. Move it into a test somewhere

Not sure there is a better or faster workflow, from deciding "Hmm I want to test against this in the future" to "make test checks this" takes something like 2 minutes with a workflow like this.

https://www.youtube.com/watch?v=F61YWNapxJg is a cool demo of this. I'm curious how well this repl assistant approach will work with larger codebases

Here's a video with a demo: https://www.youtube.com/watch?v=F61YWNapxJg

If there were an MCP to connect to, say, a running Chrome tab with your frontend running on it, that would allow an agent to both visually inspect and interact with the webpage as well as look at the network and console tabs, etc. That would be hugely helpful. Is there something like that today?

The playwright MCP

Very cool—It seems to allow access to the page itself, not to the network or console tabs, correct?

Looks good and fills a useful niche. Unfortunately for me, I only use Gemini, OpenAI, and local models and this is configured for Claude. I set a calendar reminder to see if other platforms are supported in the future.

Off topic, but I have found Google Jules to be useful for Clojure development.

It appears to have support for Gemini and OpenAI, https://github.com/bhauman/clojure-mcp?tab=readme-ov-file#ll...

I don't think the MCP is inherently tied to Claude, afaiu it should work with any agent/client that has MCP support.

Bruce recommends/mentions Claude Desktop specifically because it makes it easy to try out Clojure MCP without having to worry about token cost.

What the hell is the point of a desktop app for a chatbot? I thought there were apis for this sort of thing.

If you need to run code locally, why bother paying google to run claude?

> I highly recommend using it with Claude Desktop to start. It's prettier and there are no api charges!

???? How does this make sense to anyone

I'm not sure what you are asking exactly?

Say you have a Clojure project, it's in a folder on your computer where you likely cloned or initialized a git repo.

Now you want to leverage an agentic LLM that can connect to clojure-mcp so you can prompt the LLM to make real edits to your project source files, folder structure, resources, documentation, etc.

Your options are kind of limited: - Amazon Q CLI - Claude Code CLI - OpenAI Codex CLI

Those are the best. Then you have the IDE based ones, like Cursor, Windsurf, Copilot agent mode (in public preview currently), and so on.

What they are saying though is that Claude Desktop also support MCP, and can be used without incurring API charges.

Honestly, the in-IDE ones, for me, are not very good, you really don't need this stuff tied up inside an editor. I prefer the CLIs personally, but can see how you can also just as easily run Claude Desktop as a side-bar than need something inside your editor.

And present state isn't even that important. In 2-4 years we'll be 2 hardware generations in the future, people can buy hardware tailored to 2024-25 models and VRAM will be creeping up (or mitigations for low VRAM found). The models something uses today don't tell us much about what a project will look like in 3 years. None of the current crop of leading models are going to last that long. A project might easily be looking at the medium term not the present.

> Your options are kind of limited: - Amazon Q CLI - Claude Code CLI - OpenAI Codex CLI

Out of curiosity, which option do you go for?

For clojure-mcp, you really should try just Claude Desktop. That's because clojure-mcp provides all the tools you need already, reading files, running shell commands, running code in the REPL, running tests, listing directories, linting code, etc.

The others I listed above come with a lot of tools baked in, and I'm not sure if they could interfere, like the LLM might prefer one that's bundled to using the clojure-mcp ones.

Otherwise I use Amazon Q CLI, because it is the cheapest of the bunch. I'd say Claude Code CLI is the other I'd use personally.

> Your options are kind of limited: - Amazon Q CLI - Claude Code CLI - OpenAI Codex CLI

Ampcode have a CLI, which is their agent using Claude 4.

Google also came out with Jules a few days ago.

There's aider, with which you can use whichever LLM you'd like.

I'm pretty sure that there are others...

Aider does not have MCP support yet. Neither does Jules I believe.

Ampcode I heard of, but I also heard it's very expensive, same for Devin. I also don't know if either of them support MCP.

I'm sure there are others, of varying quality, but realistically, the options you'd want to use are the ones I listed I think.

P.S.: I'd been looking for alternatives by the way, something that lets me use OpenAI models, I've yet to try it but heard good things about: https://block.github.io/goose/

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