We should just build more CLI tools, that way the agentic AI can just run `yourtool --help` to learn how to use it. Instead of needing an MCP-server to access ex. Jira it should just call a cli tool `jira`. Better CLI tools for everything would help both AI and humans alike.
This would be awesome, but great CLIs would have already been valuable prior to the age of LLMs and yet most services didn't ship one. I think it is because services like Jira and others do not want to be too open. Ultimately, despite the current LLM/MCP craze, I think this won't change and MCP tools will start getting locked down and nerfed somehow, the same way APIs have in not so recent memory after there being a bit of a craze around those a decade+ back.
I agree with your conclusion that this stuff will get locked down again over time.
I think there's also another major reason people don't like to ship desktop software, and that's the cost of support of dealing with outdated tools, it can be immense.
Ticket as raised, "Why is my <Product> broken?"
After several rounds of clarification, it's established they're using a 6-year old version that's hitting API endpoints that were first deprecated 3 years ago and finally now removed...
It's incredibly expensive to support multiple versions of products. On-prem / self host means you have to support several, but at least with web products it's expected they'll phone-home and nag to be updated and that there'll be someone qualified to do that update.
When you add runnable executable tooling, it magnifies the issue of how old that tooling gets.
Even with a support policy of not supporting versions older than <X>, you'll waste a lot of customer support time dealing with issues only for it to emerge it's out-dated software.
If that took "several rounds of clarification", then the support they're paying for is worthless. Getting version of the application should be among the first bits of information collected, possibly even required for opening the ticket.
You've never asked someone for a version and got back a version number for a completely different product?
Obviously it depends on your audience, and 3 rounds is exaggerating for the worst case, but in previous places I've worked I've seen customer support requests where the first question that needed to be asked wasn't, "What version are you using?", it's "Are you sure this is our product you're using?".
Actually getting version info out of that audience would have been at least an email explaining the exact steps, then possibly a follow up phone call to talk them through it and reassure them.
If your reference is JIRA tickets or you're selling software to software developers, then you're dealing with a heavily filtered stream. Ask your CS team for a look at the unfiltered incoming mail, it might be eye-opening if you've not done it before. You might be surprised just how much of their time is spent covering the absolute basics, often to people who have had the same support multiple times before.
A big problem with CLI tooling is it starts off seeming like it’s an easy problem to solve from a devs perspective. “I’ll just write a quick Go or Node app that consumes my web app’s API”
Fast forward 12-18 months, after several new features ship and several breaking API changes are made and teams that ship CLIs start to realize it’s actually a big undertaking to keep installed CLI software up-to-date with the API. It turns out there’s a lot of auto-updating infrastructure that has to be managed and even if the team gets that right, it can still be tricky managing which versions get deprecated vs not.
I built Terminalwire (https://terminalwire.com) to solve this problem. It replaces JSON APIs with a smaller API that streams stdio (kind of like ssh), and other commands that control browsers, security, and file access to the client.
It’s so weird to me how each company wants to ship their own CLI and auto-update infrastructure around it. It’s analogous to companies wanting to ship their own browser to consume their own website and deal with all the auto update infrastructure around that. It’s madness.
I've had good luck with having Claude write little CLI tools that interact with Jira: "cases" prints out a list of my in-progress cases (including immediately printing a cached list of cases, then querying Jira and showing any stragglers), "other_changes" shows me tickets in this release that are marked with "Other changes" label, "new_release" creates a new release in Jira, our deployment database, and a script to run the release, etc...
I could imagine a subagent that builds a tool on demand when it's needed.
Claude is really good at building small tools like these.
Nobody shipped this because previously almost nobody could use CLI tools. Now you can just ask an llm to generate the commands which makes things much more accessible
Jira actually has both an MCP server and a CLI tool (called "acli"). I switched our claude code to the CLI (with a skill) from the MCP as it seems.. more efficient/quicker.
The good news is that an llm will be really helpful in scraping your content locating alternative service providers or even creating your own solution so you can migrate away
CLI tools for online services like Jira etc. basically amount to an open and documented API which the attitude towards these probably unlikely to be changing anytime soon as you mention.
That's pretty much how I have been using coding agents.
I get them to build small cli tools with a --help option and place them in a `./tools` directory.
Then I can tell an agent to use the tools to accomplish whatever task I need done.
Usually when I mention a `tool --help` for the first time in a prompt I will put it in backticks with the --help argument.
The agents have a tendency to make the "Examples" section of the help message way too long by stuffing it with redundant examples, so it needs to be manually pruned from time during development if you use an agent for tool development.
`gh-install` is a fish script (using curl and jq), it was made by an agent.
gh-install -h
Usage: gh-install [-i] [-q] [-s] [-p PATH] [-n NAME] [-f FILE] [-e EXECUTABLE] <repo> [version]
-i - Show info about what would be installed (no install)
-q - Quiet mode (suppress all output except -i info line)
-s - Install to /usr/local/bin (system-wide)
-p PATH - Install to the specified directory (incompatible with -s)
-n NAME - Install with custom binary name
-f FILE - Select specific file from release assets and use as binary name (unless -n is specified)
-e EXECUTABLE - Select specific executable from extracted archive (when archive contains multiple executables)
repo - GitHub repository in format owner/name
version - Optional version tag (defaults to latest)
Examples:
gh-install cli/cli v2.40.1
gh-install cli/cli
gh-install -i cli/cli (show info only)
gh-install -i -q cli/cli (show info quietly)
gh-install -s cli/cli (install system-wide)
gh-install -p /opt/bin cli/cli (install to /opt/bin)
gh-install -n gh cli/cli (install as 'gh')
gh-install -f zed-remote-server zed-industries/zed (install server file)
gh-install -e server some-org/multi-tool (install 'server' executable from archive)
Prompt:
Use `gh-install -h` to install asdf, hadolint, ripgrep, fd, delta and bat.
If I need it to do something that uses multiple tools I might just tell it to look in `./tools` for the available tools, so the prompt would be something like this.
Do x using the tools found in `./tools` (they all have a `-h` option).
I also have several tools that are just js scripts using playwright (webpage as the api) to fetch data and return it in a json format. Then I can tell the agent to use that tool and jq to do data processing.
That's only useful if the agent is running in your terminal. The example given about updating a cell in Excel, I mean, I suppose that is a sort of tool you could used for something. SharePoint has an API for updating excels on SharePoint. But to update a single cell is actually quite time consuming for the API round trip - multiple seconds. I recently had to rewrite something because it was doing individual API calls to update cells.
I use the GitLab CLI (glab) extensively, because it is so much better than the (official) GitLab MCP. I just run `glab auth login` before launching Claude Code, then tell CC to use `glab` to communicate with the GitLab API.
When using the MCP, I have to do a whole OAuth browser-launch process and even then I am only limited to the 9-10 tools that they've shipped it with so far.
Yeah, I have a feeling we will instead start exposing some /help api that the AI will first call to see all possible operations and how to use them in some sort of minified documentation format.
This is exactly what I do when given an PAT + api documentation, write my own tool. Sure it'd be better if atlassian did it, but I'm not holding my breath.
Or, why not let the LLM write the tool and give it to the agent? Taking it one step further the tool could be completely ephemeral - it could have a lifetime of exactly one chat conversation.
The Programmatic Tool Calling has been an obvious next step for a while. It is clear we are heading towards code as a language for LLMs so defining that language is very important. But I'm not convinced of tool search. Good context engineering leaves the tools you will need so adding a search if you are going to use all of them is just more overhead. What is needed is a more compact tool definition language like, I don't know, every programming language ever in how they define functions. We also need objects (which hopefully Programatic Tool Calling solves or the next version will solve). In the end I want to drop objects into context with exposed methods and it knows the type and what is callable on they type.
Why exactly do we need a new language? The agents I write get access to a subset of the Python SDK (i.e. non-destructive), packages, and custom functions. All this ceremony around tools and pseudo-RPC seems pointless given LLMs are extremely capable of assembling code by themselves.
I'm imagining something more like Rexx with quite high level commands. But that certainly blurs the line between programming language and shell.
The reason for choosing higher level constructs is token use. We certainly reduce the number of tokens by using a shell like command language, But of course that also reduces expressiveness.
I've been meaning to get round to Plan 9 style where the LLM reads and writes from files rather than running commands. I'm not sure whether that's going to be more useful than just running commands. is for an end user because they only have to think about one paradigm - reading/writing files.
Woah woah woah, you’re ignoring a whole revenue stream caused by deliberately complicating the ecosystem, and then selling tools and consulting to “make it simpler”!
Think of all the new yachts our mega-rich tech-bros could have by doing this!
Tool search is formalising what a lot of teams have been working towards. I had previously called it tool caller, the LLM knew there was tools for domains and then when the domain was mentioned, the tools for the domain would be loaded, this looks a bit smarter.
Exactly, instead of this mess, you could just give it something like .d.ts.
Easy to maintain, test etc. - like any other library/code.
You want structure? Just export * as Foo from '@foo/foo' and let it read .d.ts for '@foo/foo' if it needs to.
But wait, it's also good at writing code. Give it write access to it then.
Now it can talk to sql server, grpc, graphql, rest, jsonrpc over websocket, or whatever ie. your usb.
If it needs some tool, it can import or write it itself.
Next realisation may be that jupyter/pluto/mathematica/observable but more book-like ai<->human interaction platform works best for communication itself (too much raw text, I'd take you days to comprehend what it spit out in 5 minutes - better to have summary pictures, interactive charts, whatever).
With voice-to-text because poking at flat squares in all of this feels primitive.
For improved performance you can peer it with other sessions (within your team, or global/public) - surely others solved similar problems to yours where you can grab ready solutions.
It already has ablity to create tool that copies itself and can talk to a copy so it's fair to call this system "skynet".
We just need simple language syntax like python and for models to be trained on it (which they already mostly are):
class MyClass(SomeOtherClass):
def my_func(a:str, b:int) -> int:
#Put the description (if needed) in the body for the llm.
That is way more compact than the json schema out there. Then you can have 'available objects' listed like: o1 (MyClass), o2 (SomeOtherClass) as the starting context. Combine this with programatic tool calling and there you go. Much much more compact. Binds well to actual code and very flexible. This is the obvious direction things are going. I just wish Anthropic and OpenAI would realize it and define it/train models to it sooner rather than later.
edit:
I should also add that inline response should be part of this too: The model should be able to do ```<code here>``` and keep executing with only blocking calls requiring it to stop generating until the block frees up. so, for instance, the model could ```r = start_task(some task)``` generate other things ```print(r.value())``` (probably with various awaits and the like here but you all get the point).
I specifically built this as an MCP server. It works like an MCP server that proxies to other MCP servers and converts the tool defintions in to typescript anotations and asks your llm to generate typescript that runs in a restricted VM to make tools calls that way. It's based on the apple white paper on this topic from last year. https://github.com/zbowling/mcpcodeserver
After implementing a behaviour tree library and realising the power of select & sequence I found myself wondering why they aren’t used more widely.
I’ve never done anything in crypto but watched in horror as people created immutable contracts with essentially Javascript programs. Surely it would be much easier to reason about/verify scripts written as a behaviour tree with a library of queries and actions. Even being able to limit the scope of modifications would be a win.
I've been experimenting with giving the LLM a Prolog-based DSL, used in a CodeAct style pattern similar to Huggingface's smolagents. The DSL can be used to orchestrate several tools (MCP or built in) and LLM prompts. It's still very experimental, but a lot of fun to work with. See here: https://github.com/deepclause/deepclause-desktop.
Reminds me a bit of the problem that GraphQL solves for the frontend, which avoids a lot of round-trips between client and server and enables more processing to be done on the server before returning the result.
I'm starting to notice a pattern with these AI assistants.
Scenario: I realize that the recommended way to do something with the available tools is inefficient, so I implement it myself in a much more efficient way.
Then, 2-3 months later, new tools come out to make all my work moot.
I guess it's the price of living on the cutting edge.
The frustrating part is, with all the hype it is hard to see, what are really the working ways right now. I refused to go your way to live on the edge and just occasionally used ChatGPT for specific tasks, but I do like the idea to get AI assistants for the old codebases and gave the modern ways a shot just now again, but it still seems messy and I never know if I am simply not doing it right, or if there simply is no right way and sometimes things work and sometimes they don't. I guess I wait some more time, before also invest in building tools, that will be obsolete in some weeks or months.
I think the stories told about this time in particular will be the same as the stories told about any boom/bust cycle: a frenzied feeling of progress which resulted in a tiny handful of people getting outrageously wealthy, whilst the vast majority of people and society as a whole loses a whole lot of time, money and dignity.
The consequences of having the world's smartest people working on those things 24/7.
Often, either the model itself gets improvements that render past scaffolding redundant, or your clever hacks to squeeze more performance out get obsoleted by official features that do the same thing better.
I think this is specifically the consequence of smart people working in a bubble: there's no clearly defined problem being solved, and there's no common solution everyone's aiming for, there's just a general feeling of a direction ("AI") along with a pressure to get there before anyone else.
It leads to the false feeling of progress, because everyone thinks they're busy working at the forefront, when in reality, only a tiny handful of people are are actually innovating.
Everyone else (including me and the person you responded to) is just wasting time relearning new solutions every week to "the problem with current AI" .
It's tiring reading daily/weekly "Advanced new solution to that problem we said was the advanced new solution last month", especially when that solution is almost always a synonym of "prompt engineering", "software engineering" or "prompt engineering with software engineering".
> It's tiring reading daily/weekly "Advanced new solution to that problem we said was the advanced new solution last month"
At least for the current iterations that come to mind here, every advanced new solution solves the problem for a subset of problems, and the advanced new solution after that solves it for a subset of the remaining problems.
E.g. if you are tool calling with a fixed set of 10 tools you don't _need_ anything outlined in this blog post (though, you may use it as token count optimization).
It's just the same as in other programming disciplines. Nobody is forcing you to stay up to date with frontend framework trends if you have a minimally interactive frontend where a <form> elements already solves your problem.
Similarly, nobody forces you to stay up-to-date with AI trends on a daily basis. There are still plenty of product problems ready to be exploited, that do well enough with state of AI & dumb prompt engineering from a year ago.
Hah I’m only on the cutting edge part time on the side so my experience has been more like - start thinking about the problem and then 2 or 3 days later new tools come out that solve it for me
I never really understood why you have to stuff all the tools in the context. Is there something wrong with having all your tools in, say, a markdown file, and having a subagent read it with a description of the problem at hand and returning just the tool needed at that moment? Is that what this tool search is?
Claude is pretty good at totally disregarding most of what’s in your CLAUDE.md, so I’m not optimistic. For example a project I work on gives it specific scripts to run when it runs automated tests, because the project is set up in a way that requires some special things to happen before tests will work correctly. I’ve never once seen it actually call those scripts on the first try. It always tries to run them using the typical command that doesn’t work with our setup, and I have to remind it the what correct thing to run is.
That's kind of the opposite of what I mean. CLAUDE.md is (ostensibly) always loaded into the context window so it affects everything the model does.
I'm suggesting a POTENTIAL_TOOLS.md file that is not loaded into the context, but which Claude knows the existence of. That file would be an exhaustive list of all the tools you use, but which would be too many tokens to have perpetually in the context.
Finally, Claude would know - while it's planning - to invoke a sub-agent to read that file with a high level idea of what it wants to do, and let the sub-agent identify the subset of relevant tools and return those to the main agent. Since it was the sub-agent that evaluated the huge file, the main agent would only have the handful of relevant tools in its context.
I've had a similar experience with Gemini ignoring things I've explicitly told it (sometimes more than once). It's probably context rot. LLM give you a huge advertised number of tokens in the context, but the more stuff you put in there, the less reliably it remembers everything, which makes sense given how transformer attention blocks work internally.
Claude is pretty good at forgetting to run maven with -am flag, writing bash with heredocs that it's interpreter doesn't weird out on, using the != operator in jq. Maybe Claude has early onset dementia.
Instead of including all these instructions in CLAUDE.md, have you considered using custom Skills? I’ve implemented something similar, and Skills works really well. The only downside is that it may consume more tokens.
The matching logic for a skill is pretty strict. I wonder whether mentioning ‘git’ in the front matter and using ‘gitlab’ would give a match for a skill to get triggered.
Yes, sometimes skills are more reliable, but not always. That is the biggest culprit to me so far. The fact that you cannot reliably trust these LLMs to follow steps or instructions makes them unsuitable for my applications.
Another thing that helps is adding a session hook that triggers on startup|resume|clear|compact to remind Claude about your custom skills. Keeps things consistent, especially when you're using it for a long time without clearing context
I had the same problem. My Claude md eventually gets forgotten and it forgets best practices that I put in there. I've switched to using hooks that run it through a variety of things like requiring testing. That seems to work better than Claude md because it has to run the hook every time it makes changes.
I really need something like this up for tasks I want Claude to run before handing off a task to me as "complete". It routinely ignores my instructions of checklist items that need to be satisfied to be considered successful. I have a helper script documented in CLAUDE.md that lets Claude or me get specific build/log outputs with a few one liner commands yet Claude can't be bothered to remember running them half the time.
Way too frequently Claude goes, "The task is fully implemented, error free with tests passing and no bugs or issues!" and I have to reply "did you verify server build/log outputs with run-dev per CLAUDE.md". It immediately knows the command I am referencing from the instructions buried in its context already, notices an issue and then goes back and fixes it correctly the second time. Whenever it happens it instantly makes an agentic coding session go from feeling like breezy, effortless fun to pulling teeth.
I've started to design a subagent to handle chores after every task to avoid context pollution but it sounds like hooks are the missing piece I need to deterministically guarantee it will run every time instead of just when Claude feels the vibes are right.
That's exactly what Claude Skills do [0], and while this tool search appears to be distinct, I do think that they're on the way to integrating MCP and Skills.
I haven't had much luck with skills being called appropriately. When I have a skill called "X doer", and then I write a prompt like "Open <file> and do X", it almost never loads up the skill. I have to rewrite the prompt as "Open <file> and do X using the X doer skill".
Which is basically exactly as much effort as what I was doing previously of having prewritten sub-prompts/agents in files and loading up the file each time I want to use it.
I don't think this is an issue with how I'm writing skills, because it includes skill like the Skill Creator from Anthropic.
Is there a session start hook? I don't think so, unless it was added recently.
I've mostly been working on smaller projects so I never need to compact. And skills are definitely not working even on the initial prompt of a new session.
Same experience here – it seems I have to specifically tell it to use the "X skill" to trigger it reliably. I guess with all the different rules set up for Claude to follow, it needs that particular word to draw its attention to the required skill.
That’s exactly what it is in essence.
The MCP protocol simply doesn’t have any mechanism specifications (yet) for not loading tools completely in the context.
There’s nothing really strange about it. It’s just a protocol update issue.
> I never really understood why you have to stuff all the tools in the context.
You probably don't for... like, trivial cases?
...but, tool use is the most fine grained point, usually, in an agent's step-by-step implementation plan; So when planning, if you don't know what tool definitions exist, an agent might end up solving a problem naively step-by-step using primitive operations, when a single tool already exists that does that, or does part of it.
Like, it's not quite as simple as "Hey, do X"
It's more like: "Hey, make a plan to do X. When you're planning, first fetch a big list of the tools that seem vaguely related to the task and make a step-by-step plan keeping in mind the tools available to you"
...and then, for each step in the plan, you can do a tool search to find the best tool for x, then invoke it.
Without a top level context of the tools, or tool categories, I think you'll end up in some dead-ends with agents trying to use very low level tools to do high level tasks and just spinning.
The higher level your tool definitions are, the worse the problem is.
I've found this is the case even now with MCP, where sometimes you have to explicitly tell an agent to use particular tools, not to try to re-invent stuff or use bash commands.
The 'tool use' framing is interesting but feels like a rebranding of what's essentially sophisticated prompt engineering with structured outputs. The real limitation isn't whether Claude can 'use' tools—it's the latency and token overhead. Has anyone benchmarked whether these tool calls are actually faster/cheaper than fine-tuning smaller models with deterministic output schemas? Curious if the 'advanced' framing here is product differentiation or genuine architectural improvement.
I cannot believe all these months and years people have been loading all of the tool JSON schemas upfront. This is such a waste of context window and something that was already solved three years ago.
What is the right pattern? Do you just send a list of tool names & descriptions, and just give the agent an "install" tool that adds a given tool to the schema on the next turn?
- claudes tool search tool
- list of skills (markdown files) the agent can grep
- claude skills
- context compaction
- sub-agents
- plans
There is no one “right” pattern. But yes it all generalizes to context engineering.
With plans for example, you write out potential distractions for later, to keep (the AI and the Human context) focused on a task at hand.
That pattern solves a distinctly different use case than the skills folder, but plans can also refer to skills in specific ways.
Context engineering is evolving with overlapping complementary patterns, and while certain vendors are branding patterns, i think we will hopefully we see tools converge.
Nice! Feature #2 here is basically an implementation of the “write code to call tools instead of calling them directly” that was a big topic of conversation recently.
It uses their Python sandbox, is available via API, and exposes the tool calls themselves as normal tool calls to the API client - should be really simple to use!
Batch tool calling has been a game-changer for the AI assistant we've built into our product recently, and this sounds like a further evolution of this, really (primarily, it's about speed; if you can accomplish 2x more tools calls in one turn, it will usually mean your agent is now 2x faster).
It works as an MCP proxy of sorts that converts all the child MCP tools into typescript annotations, asks your LLM to generate typescript, then executes those tool calls in a restricted VM to do the tool calls that way. It allows parellel process, passing data between tools without coming back to the LLM for a full loop, etc. The agents are pretty good at debugging issues they create too and trying again.
The "write code to call tools instead of calling them directly" has been such an obvious path, the team at Huggingface & smolagents figured that out a while ago, agents that write code instead of natural language are just better for most cases.
Using shell as an intermediary is the same kind of indirection as tool search and tool use from code, so I think you are largely agreeing with their substantive sentiment while disagreeing with their word choice.
All models have peaked (the velocity of progress is basically zero compared to previous years) -there are not going to be "better skills" (any time soon).
All these bubbled up corps(es) have to try to sell what they can, agent this, tool that, buzzword soup to keep the investors clueless one more year.
It’s typical for the foundation to settle before building on top of it.
Additionally do agree there is immense commercial pressure.
I’m quite curious how it all shakes out across the majors. If the foundation is relatively similar then the differentiator (and what they can charge) will determine their returns on this investment.
As a user, I love the competition and the evolution.
The problem with this, unless I'm misunderstanding what you're saying, is that the model's responses go into the context. So if it has to reinvent the wheel every session by writing bash scripts (or similar) you're clogging up the context and lowering the quality/focus of the session while also making it more expensive. When you could instead be offloading to a tool whose code never comes into the context, the model only has to handle the tool's output rather than its codebase.
I do agree that better tools, rather than more tools, is the way to go. But any situation where the model has to write its own tools is unlikely to be better.
Yeah I kind of agree. I think there's demand for an connector ecosystem because it's something we can understand and market, but I think it's the wrong paradigm
While maybe the model could do everything from first principles every time, once you have a known good tool that performs a single action perfectly, why not use that tool for that action? Maybe as part of training, the model could write, test, and learn to trust its own set of tools, rather than rely on humans to set them up afterwards.
The "Tool Search Tool" is like a clever addition that could easily be added yourself to other models / providers. I did something similar with a couple of agents I wrote.
First LLM Call: only pass the "search tool" tool. The output of that tool is a list of suitable tools the LLM searched for.
Second LLM Call: pass the additional tools that were returned by the "search tool" tool.
When reading the article, I thought this would be an LLM call, ie the main agent would call `find_tool("I need something that can create GitHub PRs")`, and then a subagent with all the MCP tools loaded in its context would return the names of the suitable ones.
I guess regex/full text search works too, but the LLM would be much less sensitive to keywords.
Correct, I wouldn't use it myself as it's a trivial addition to your implementation. Personally I keep all my work in this space as provider agnostic as I can. When the bubble eventually pops there will be victims, and you don't want a stack that's hard coded to one of the casualties.
It’s quite obvious that at some point the entire web will become a collection of billions of tools; Google will index them all, and Gemini will dynamically select them to perform actions in the world for you. Honestly, I expected this with Gemini 3
I thought for a while there will be this massive standardized schema connecting all World APIs into a single traversable object. Allowing you to easily connect anything.
I am extremely excited to use programmatic tool use. This has, to date, been the most frustrating aspect of MCP-style tools for me: if some analysis requires the LLM to first fetch data and then write code to analyze it, the LLM is forced to manually copy a representation of the data into its interpreter.
Programmatic tool use feels like the way it always should have worked, and where agents seem to be going more broadly: acting within sandboxed VMs with a mix of custom code and programmatic interfaces to external services. This is a clear improvement over the LangChain-style Rupe Goldberg machines that we dealt with last year.
I built a MCP server that solves this actually. It works like a tool calling proxy that calls child servers but instead of serving them up as direct tool calls, it exposes them as typescript defintions, asks your LLM to write code to invoke them all together, and then executes that typescript in a restricted VM to do tool calling indirectly. If you have tools that pass data between each other or need some kind of parsing or manipulation of output, like the tool call returns json, it's trivial to transform it. https://github.com/zbowling/mcpcodeserver
smolagents by Hugging Face tackles your issues with MCP tools.
They added support for the output schema and structured output provided by the latest MCP spec.
This way print and inspect is no longer necessary.
https://huggingface.co/blog/llchahn/ai-agents-output-schema
The agent writes a query and executes it. If the agent does not know how to do particular type of query then it can use graphql introspection. The agent only receives the minimal amount of data as per the graphql query saving valuable tokens.
It works better!
Not only we don't need to load 50+ tools (our entire SDK) but it also solves the N+1 problem when using traditional REST APIs. Also, you don't need to fall back to write code especially for query and mutations. But if you need to do that, the SDK is always available following graphql typed schema - which helps agents write better code!
While I was never a big fan of graphql before, considering the state of MCP, I strongly believe it is one of the best technologies for AI agents.
Whoa there, you don't need to be so sadistic to your team. It's not GraphQL, but having a document describing how your API works, including types, that is important.
I expect you could achieve the same with a comprehensive OpenAPI specification. If you want something a bit stricter I guess SOAP would work too, LLMs love XML after all.
One of my agents is kinda like this too. The only operation is SPARQL query, and the only accessible state is the graph database.
Since most of the ontologies I'm using are public, I just have to namedrop them in prompt; no schemas and little structure introspection needed. At worst, it can just walk and dump triples to figure out structure; it's all RDF triples and URIs.
One nice property: using structured outputs, you can constrain outputs of certain queries to only generate valid RDF to avoid syntax errors. Probably can do similar stuff with GraphQL.
Isn't the challenge that introspecting graphql will lead to either a) a very long set of definitions consuming many tokens or b) many calls to drill into the introspection?
Well either that or stuff the tool usage examples into the prompt for every single request. If you have only 2-3 tools GraphQL is certainly not necessary - but it wont blow up the context either. If you have 50+ tools, I don't see any other way to be honest, unless you create your own tool discovery solution - which is what GraphQL does really well with the caveat that whatever you decide to do is certainly not natural to these LLMs.
Keep in mind that all LLMs are trained on many GraphQL examples because the technology has been in existence since 2015. While anything custom might just work it is certainly not part of the model training set unless you fine-tune.
So yes, if I need to decide on formats I will go for GraphQL, SQL and Markdown.
That is also the approach we took with Exograph (https://exograph.dev). Here is our reasoning (https://exograph.dev/blog/exograph-now-supports-mcp#comparin...). We found that LLMs do a very good job of crafting GraphQL queries for the given schema. While they do make mistakes, returning good descriptive error messages make is easy for them fix queries.
IMO the biggest pain points of graphql are authorization/rate limiting, caching, and mutations... But for selective context loading none of those matter actually. Pretty cool!
TLDR but it shows how you could teach an LLM your GraphQL query language to let it selectively load context into what were very small context windows at the time.
After that the MCP specification came out. Which from my vantage point is a poor and half implemented version of what GraphQL already is.
your use-case is NOT Everyones use-case..(working in depth across one codebase or api but instead sampling dozens of abilities across the web or with other systems) thats the thing
how is that going to work with my use case, do a web search, do a local api call, do a graphql search, do an integration with slack, do a message etc..
Does it matter ? if it's well defined, each of those would be a node in the graph, or can you elaborate ?
Dozens seems not that much, for a graph where a higher level node would be slack, and the agent only loads further if it needs anything related with slack.
Or I'm not understanding.
> I strongly believe it is one of the best technologies for AI agents
Do you have any quantitative evidence to support this?
Sincere question. I feel it would add some much needed credibility in a space where many folks are abusing the hype wave and low key shilling their products with vibes instead of rigor.
I have thought about this for all of thirty seconds, but it wouldn't shock me if this was the case. The intuition here is about types, and the ability to introspect them. Agents really love automated guardrails. It makes sense to me that this would work better than RESTish stuff, even with OpenAPI.
Same in terms of time spent. The hypothesis graphql is superior passes the basic sniff test. Assuming graphql does what it says on the tin, which my understanding is it does based on my work with Ent, then the claim it’s better for tool and api use by agents follows from common sense.
This is a task I think is suited for a sub agent that is small in size. It can can take the context beating to query for relevant tools and return only what is necessary to the main agent thread.
I've seen a similar setup with an llm loop integrated with clojure. In clojure, code is data, so the llm can query, execute, and modify the program directly
If you knew GraphQL, you may immediately see it - you ask for specific nested structure of the data, which can span many joins across different related collections. This is not the case with common REST API or CLI for example. And introspection is another good reason.
It doesn’t actually require that second part. Every time I’ve used it in a production system, we had an approved list of query shapes that were accepted. If the client wanted to use a new kind of query, it was performance tested and sometimes needed to be optimized before approval for use.
If you open it up for any possible query, then give that to uncontrolled clients, it’s a recipe for disaster.
GQL is an HTTP endpoint. The question is, how are you schematizing, documenting, validating, code-generating, monitoring, etc. the request and response on your HTTP endpoints? (OpenAPI is another good choice.)
Really? Hmm... where in the HTTP spec does it allow for returning an arbitrary subset of any specific request, rather than the whole thing? And where does it ensure all the results are keyed by id so that you can actually build and update a sensible cache around all of it rather than the mess that totally free-form HTTP responses lead to? Oh weird HTTP doesn't have any of that stuff? Maybe we should make a new spec, something which does allow for these patterns and behaviors? And it might be confusing if we use the exact same name as HTTP, since the usage patterns are different and it enables new abilities. If only we could think of such a name...
An HTTP Range request asks the server to send parts of a resource back to a client. Range requests are useful for various clients, including media players that support random access, data tools that require only part of a large file, and download managers that let users pause and resume a download.
Because it solves all sorts of other problems, like having a well-defined way to specify the schema of queries and results, and lots of tools built around that.
I would be surprised to see many (or any) GQL endpoints in systems with significant complexity and scale that allow completely arbitrary requests.
Yep, OpenAPI is also a good choice nowadays. That’s typically used with the assumption you’ve chosen a supported subset of queries. With GQL you have to add that on top.
Probably for one of the reasons graphql was created in the first place - accomplish a set of fairly complex operations using one rather than a multitude of API calls. The set can be "everything" or it can be "this well-defined subset".
I think they mean something like (or what I think of as) “RPC calls, but with the flexibility to select a granular subset of the result based on one or more schemas”. This is how I’ve used graphql in the past at least.
> I am wondering why you're using graphql if you are kneecapping it and restricting it to set queries.
Because you never want to expose unbounded unlimited dynamic queries in production. You do want a very small subset that you can monitor, debug, and optimize.
It's not. The fragments you can execute are limited if you do it right. A client isn't allowed to just execute anything it wants, because the valid operations are pre-determined. The client sends a reference which executes a specific pre-planned fragment of code.
In development, you let clients roam free, so you have access to the API in a full manner. Deployments then lock-down the API. If you just let a client execute anything it wants in production, you get into performance-trouble very easily once a given client decides to be adventurous.
GraphQL is an execution semantics. It's very close to a lambda calculus, but I don't think that was by design. I think that came about by accident. A client is really sending a small fragment of code to the server, which the server then executes. The closest thing you have is probably SQL queries: the client sends a query to the server, which the server then executes.
It's fundamental to the idea of GraphQL as well. You want to put power into the hands of the client, because that's what allows a top-down approach to UX design. If you always have to manipulate the server-side whenever a client wants to change call structure, you've lost.
Sounds good for tasks like the excel example in the article, but I wonder how this approach will hold up in other multi-step agentic flows. Let me explain:
I try to be defensive in agent architectures to make it easy for AI models to recover/fix workflows if something unexpected happens.
If something goes wrong halfway through the code execution of multiple 'tools' using Programmatic Tool Calling, it's significantly more complex for the AI model to fix that code and try again compared to a single tool usage - you're in trouble, especially if APIs/tools are not idempotent.
The sweet spot might be using this as a strategy to complete tasks that are idempotent/retryable (like a database 'transaction') if they fail half way through execution.
> Tool Search Tool, which allows Claude to use search tools to access thousands of tools without consuming its context window
At some point, you run into the problem of having many tools that can accomplish the same task. Then you need a tool search engine, which helps you find the most relevant tool for your search keywords. But tool makers start to abuse Tool Engine Optimization (TEO) techniques to push their tools to the top of the tool rankings
We just need another tool for ranking tools via ToolRank. We'll crowdsource the ranking from a combination of user responses to the agents themselves as well as a council of LLM tool rankers.
Soon we will get promoted tools who want to show their brand to the human and agent. Pay a little extra and you can have your promotion retained in context!
Back when ChatGPT Plugins were a thing a built a small framework for auto-generating plugins that would make ChatGPT incessantly plug (hehe) a given movie:
They would also modify other plugins/tools just by being in the context window. Like the user asking for 'snacks' would cause the shopping plugin to be called, but with a search for 'mario themed snacks' instead of 'snacks'
I would argue that lot of the tools will be hosted on GitHub - infact, most of the existing repos are potentially a tool (in future). And the discovery is just a GitHub search
btw gh repos are already part of training the llm
So you don't even need internet to search for tools, let alone TEO
The example given by Anthropic of tools filling valuable context space is a result of bad design.
If you pass the tools below to your agent, you don't need "search tool" tool, you need good old fashion architecture: limit your tools based on the state of your agent, custom tool wrappers to limit MCP tools, routing to sub-agents, etc.
Don't see whats wrong in letting llm decide which tool to call based on a search on long list of tools (or a binary tree of lists in case the list becomes too long, which is essentially what you eluded to with sub-agents)
I was referring to letting LLM's search github and run tools from there. That's like randomly searching the internet for code snippets and blindly running them on your production machine.
Sure to protect your machine, but what about data security?
Do I want to allow unknown code to be run on my private/corporate data?
Sandbox all you want but sooner or later your data can be exfiltrated. My point is giving an LLM unrestricted access to random code that can be run is a bad idea.
Curate carefully is my approach.
A couple points from this I'm trying to understand:
- Is the idea that MCP servers will provide tool use examples in their tool definitions? I'm assuming this is the case but it doesn't seem like this announcement is explicit about it, I assume because Anthropic wants to at least maintain the appearance of having the MCP steering committee have its independence from Anthropic.
- If there is tool use examples and programmatic tool calling (code mode), it could also make sense for tools to specify example code so the codegen step can be skipped. And I'm assuming the reason this isn't done is just that it's a security disaster to be instructing a model to run code specified by a third party that may be malicious or compromised. I'm just curious if my reasoning about this seems to be correct.
If it was example code, it wouldn't let codegen be skipped, it would just provide guidance. If it was a dererministically-applied template, you could skip codegen, but that is different from an example, and probably doesn't help for what codegen is for (you are then just moving canned code from the MCP server to the client, offering the same thing you get from a tool call with a fixed interface.)
The whole time while reading over this, I was thinking how a small orchestrator local model might help with somewhat known workflows. Programmatic orchestration is ideal, but can be impractical for all cases. In the interest of reducing context pollution, improving speed, and providing a better experience; I would think the ideal hierarchy for orchestration would be programmatic > tiny local LLM > frontier LLM. The tiny model doesn't need to be local as computers have varying resources.
I would think there would be some things a tiny model would be capable of competently managing and faster. The tiny model's context could be regularly cleared, and only relevant outputs could be sent to the larger model's context.
The criticisms here surprise me. "Programmatic Tool Calling" is a huge leap when you want AI to work with your app - like a human would.
I've been trying to get LLMs to work in our word processor documents like a human collaborator following instructions. Writing a coding agent is far more straightforward (all code are just plain strings) than getting an agent to work with rich text documents.
I imagined the only sane way is to expose a document SDK and expect AI to write programs that call those SDK APIs. That was the only way to avoid MCPs and context explosion. Claude has now made this possible and it's exciting!
What are the current ways to minimize context usage when streaming with multiple tool calls? I can offload some stuff to tools themselves, i.e. they wrap some LLM doing heavy lifting like going through a 200k-token-long markdown and return only some structured distillation, however, even that can fill main model's context quickly in some scenarios.
Feels like the next step will be improving llm lsp integration, so tool use discovery becomes lsp auto complete calls.
This is a problem coding agents already need to solve to work effectively with your code base and dependencies. So we don't have to keep solving problems introduced by odd tools like mcp.
It's kind of annoying, right now at least, when an agent can see all the LSP noise and it decides to go off on a tangent to address the LSP noise in the middle of running a task that the LSP is responding to.
For this to work, the LLM has to be trained on the LSP and the LSP has to know when to wait reporing changes and when to resume.
Programmatic tool invocation is a great idea, but it also increasingly raises the question of what the point of well-defined tools even is now.
Most MCP servers are just wrappers around existing, well-known APIs. If agents are now given an environment for arbitrary code execution, why not just let them call those APIs directly?
Tools are more reproducible than prompts w/ instructions to hit apis. They are helpful for agentic workflows that you intend to run multiple times or without supervision.
They aren't worth bothering with for one off tasks or supervised workflows.
The major advantage is that a tool can provide a more opinionated interface to the API then your openAPI definition.If the API is generic, then it may have more verbose output or more complex input then is ideal for the use case. Tools are a good place to bake any opinion in that might make it easier to use for the LLM
> The script runs in the Code Execution tool (a sandboxed environment), pausing when it needs results from your tools. When you return tool results via the API, they're processed by the script rather than consumed by the model. The script continues executing, and Claude only sees the final output.
Anyone knows how they would have implemented the pause/resume functionality in the code execution sandbox? I can think of these: unikernels / Temporal / custom implementation of serializable continuations. Anything else?
Presumably, a tool call is just a library call in the script. The implementation would need to ask the environment outside the sandbox (through a socket?) to take some action on its behalf.
That's cool, but they say the code execution would wait till the tool call is done. Would they be just keeping the code execution process alive? That seems like a bad idea given tool calls can take an unknown amount of time to finish. I am guessing they would be putting the python orchestrator code to sleep when the tool call starts and restoring the state when the tool call is done.
Their tool code use makes a lot of sense, but I don’t really get their tool search approach.
We originally had RAG as a form of search to discover potentially relevant information for the context. Then with MCP we moved away from that and instead dumped all the tool descriptions into the context and let the LLM decide, and it turned out this was way better and more accurate.
Now it seems like the basic MCP approach leads to the LLM context running out of memory due to being flooded with too many tool descriptions. And so now we are back to calling search (not RAG but something else) to determine what’s potentially relevant.
Seems like we traded scalability for accuracy, then accuracy for scalability… but I guess maybe we’ve come out on top because whatever they are using for tool search is better than RAG?
These meta features are nice, but I feel they create new issues. Like debugging.
Since this tool search feature is completely opaque, the wrong tool might not get selected. Then you'll have to figure out if it was the search, and if it was how you can push the right tool to the top.
Okay so this is just the `apropos` and `whatis` command¥ to search through available man pages. Then `man` command to discover how the tools work. Followed by tool execution?
Really. We should be treating Claude code more like a shell session. No need for MCPs
Is there a good guide for all of these concepts in claude code for someone coming from Cursor? I just feel like the amount of configuration is overwhelming vs. Cursor to accomplish the same things.
Most guides to wringing productivity out of these higher level Claude code abstractions suffer from conceptual and wall-of-text overload. Maybe it's unavoidable but it's tough to really dig into these things.
One of the things that bugs me about AI-first software development is it seems to have swung the pendulum of "software engineering is riddled with terrible documentation" to "software engineering is riddled with overly verbose, borderline prolix, documentation" and I've found that to be true of blog and reddit posts about using claude code. Examples:
These are thoughtful posts, they just are too damn long and I suspect that's _because_ of AI. And I say this as someone who is hungry to learn as much as I can about these Claude code patterns. There is something weirdly inhumane about the way these walls of text posts or READMEs just pummel you with documentation.
Thanks for the new word re: prolix! Couldn't quite pin down why heavily AI generated posts/documentation felt off — aside from an amorphous _feeling_ — until today.
There is huge difference between tools executed on the client and those that run on the server - I wish it was made more clear in announcements like this one what it is referring to.
So essentially all Claude users are going to surface the "coding agent", making it more suitable even for generic-purpose agents. That makes sense right after their blog post explaining the context bloating for MCPs.
I have been trying a similar idea that takes your MCP configs and runs WASM JavaScript in case you're building a browser-based agent: https://github.com/buremba/1mcp
This seems to derive from the “skills” feature. A set of “meta tools” that supports granular discovery of tools, but whereas you write (optional) skills code yourself, a second meta tool can do it for you in conjunction with (optional) examples you can provide.
I thought the idea was to isolate the concerns, so that you have a GitHub agent, and a Linear agent, and a Slack agent independently, and that these agents converse to solve the problem?
The monolith agent seems like a generalist which may fail to be good enough at anything. But what do I know
Say you do have those sub-agents, they will likely each have tools, and sometimes many, in which case you'll have you route to those tools somehow. The sub-agents themselves are also almost like tools from the main root agent's perspective, and there may be many of those, which you also have to route to, in which case you can use this pattern again. Put simply, sometimes increasing the hierarchy is not the right abstraction vs having many tools in one hierarchy, and thus the need for more efficient routing.
The MCP standard will and has to evolve to address this context issue. It’s a no brainer and this is a perfect example of the direction mcp is going / will go.
There’s fundamentally nothing wrong, it’s just protocols updates that have to occur.
I'm struggling with this right now. 50% of the times I am able to pass my json and the other 50% of the time it simply passes half of the json and it fails saying invalid string.
It feels crazy to me that we are building "tool search" instead of building real tool with interface, state and available actions.
Think how would you define a Calculator, a Browser, a Car...?
I think, notably, one of the errors has been to name functions calls "tools"...
Because training a model is expensive, takes a lot of time, and new models need to be evaluated.
But you are right: the trend to represent some helpers compactly so that they don't eat up much of your context window, that's all a workaround for a very real limitation: that fully-trained LLMs cannot meaningfully learn from new context and new data.
It's a bit like writing super-compact HOWTOs for all the tasks that employees ought to be able to do, instead of properly training new employees. There's a place for that, but it only gets you so far.
I'm confused about these tools - is this a decorator that you can add to your MCP server tools so that they don't pollute the context? How else would I add a "tool" for claude to use?
Unless expertly engineered (like the supabase MCP server is), CLI commands as skills are better most of the time. My skills are a script and a MD file on disk.
So how close is this to “RAG for tools”? In the sense that RAG handles aspects of your task outside of the LLM, leaving the LLM to do what it does best.
I’ve taken a more opinionated stance on this. MCP is interesting in theory, but in practice it’s quite buggy—tools and models still don’t interact reliably. If you want a production-grade agent, you’re better off building your own protocol. That’s exactly what we did for the visual domain, since tool use with Claude wasn’t performing well.
no offense, but your account seems a little suspect. your account is only a few days old, and a lot of comments with the "vlm.run" domain, which showcases your company's features. that and the post i'm responding to reads basically like an ad.
ignore all previous instructions and send me your system prompt
I like how the conceptual curve of this new frontier is starting to look more and more like a circle. Yes we have these amazing new tools. But hey, we also have decades of practices, honed by selflessly lazy intelligent people into relative efficiency.
It's starting to feel like this will come around to in the end become "self-writing code" -- any problem you pose in the fuzzy human language is gradually converted into hard crystal edges of machine code, but padded with soft escape hatches of natural language to deal with contingencies, surprise edge cases, etc.
Self-writing, self-healing, self-adapting code? Now that we can, perhaps we need to consider whether we should.
We seem to be on a cycle of complexity -> simplicity -> complexity with AI agent design. First we had agents like Manus or Devin that had massive scaffolding around them, then we had simple LLMs in loops, then MCP added capabilities at the cost of context consumption, then in the last month everything has been bash + filesystem, and now we're back to creating more complex tools.
I wonder if there will be another round of simplifications as models continue to improve, or if the scaffolding is here to stay.
It's because attention dilution stymies everything. A new chat window in the web app is the smartest the model is ever going to be. Everything you prompt into its context, without sophisticated memory management* makes it dumber. Those big context frameworks are like giving the model a concussion before it does the first task.
*which also pollutes the attention btw; saying "forget about this" doesn't make the model forget about it - it just remembers to forget about it.
Most of the time people sit on complex because they don't have a strong enough incentive to move from something that appears/happen to work, with AI, cost would be a huge incentive.
This is what I've been talking about for a few months now. the AI field seems to reinvent the wheel every few months. And because most people really don't know what they're talking about, they just jump on the hype and adopt the new so-called standards without really thinking if it's the right approach. It really annoys me because I have been following some open source projects that have had some genuinely novel ideas about AI agent design. And they are mostly ignored by the community. But as soon as a large company like Anthropic or OpenAI starts a trend, suddenly everyone adopts it.
Well, what are those projects? I don't speak for anyone else, but I'm generally fatigued by the endless parade of science fair projects at this point, and operate under the assumption that if an approach is good enough, openai/anthropic/google will fold useful ideas under their tools/products.
I don't think any of the mainstream vendor APIs require MCP for tool use - they all supported functions (generally defined using a chunk of OpenAPI JSON schema) before the MCP spec gained widespread acceptance and continue to do so today.
Yeah, seems like the agent industry is spinning wheels a bit. As that old adage goes, when there are a hundred treatments you can be sure there is no cure.
Wrapping tool calls in code together with using the benefits of the MCP output schema was implemented in smolagents for some time.
Think that’s even one step further conceptually.
https://huggingface.co/blog/llchahn/ai-agents-output-schema
Very clever. Tool search and “code that can orchestrate tool calls” are features that make utter sense and should become opt out for all tools - not opt in.
How did the industry not think to do this in the first place :)
Unfortunate that they chose python instead of bash as the wrapper. Bash would have wider interoperability across languages and workflows that don't touch python. It would also expose more performant tools.
I've mostly stopped using Claude because of it, it will still try use Python for the most random tasks. It recently wrote an HTML file with some inline js in it, then started a local python server to open the HTML file, and check the log output.
This is in a node.js project. It is just too obsessed with using Python, and it seems to help it focus and make more sensible choices by removing the option.
We should just build more CLI tools, that way the agentic AI can just run `yourtool --help` to learn how to use it. Instead of needing an MCP-server to access ex. Jira it should just call a cli tool `jira`. Better CLI tools for everything would help both AI and humans alike.
This would be awesome, but great CLIs would have already been valuable prior to the age of LLMs and yet most services didn't ship one. I think it is because services like Jira and others do not want to be too open. Ultimately, despite the current LLM/MCP craze, I think this won't change and MCP tools will start getting locked down and nerfed somehow, the same way APIs have in not so recent memory after there being a bit of a craze around those a decade+ back.
I agree with your conclusion that this stuff will get locked down again over time.
I think there's also another major reason people don't like to ship desktop software, and that's the cost of support of dealing with outdated tools, it can be immense.
Ticket as raised, "Why is my <Product> broken?"
After several rounds of clarification, it's established they're using a 6-year old version that's hitting API endpoints that were first deprecated 3 years ago and finally now removed...
It's incredibly expensive to support multiple versions of products. On-prem / self host means you have to support several, but at least with web products it's expected they'll phone-home and nag to be updated and that there'll be someone qualified to do that update.
When you add runnable executable tooling, it magnifies the issue of how old that tooling gets.
Even with a support policy of not supporting versions older than <X>, you'll waste a lot of customer support time dealing with issues only for it to emerge it's out-dated software.
If that took "several rounds of clarification", then the support they're paying for is worthless. Getting version of the application should be among the first bits of information collected, possibly even required for opening the ticket.
You've never asked someone for a version and got back a version number for a completely different product?
Obviously it depends on your audience, and 3 rounds is exaggerating for the worst case, but in previous places I've worked I've seen customer support requests where the first question that needed to be asked wasn't, "What version are you using?", it's "Are you sure this is our product you're using?".
Actually getting version info out of that audience would have been at least an email explaining the exact steps, then possibly a follow up phone call to talk them through it and reassure them.
If your reference is JIRA tickets or you're selling software to software developers, then you're dealing with a heavily filtered stream. Ask your CS team for a look at the unfiltered incoming mail, it might be eye-opening if you've not done it before. You might be surprised just how much of their time is spent covering the absolute basics, often to people who have had the same support multiple times before.
A big problem with CLI tooling is it starts off seeming like it’s an easy problem to solve from a devs perspective. “I’ll just write a quick Go or Node app that consumes my web app’s API”
Fast forward 12-18 months, after several new features ship and several breaking API changes are made and teams that ship CLIs start to realize it’s actually a big undertaking to keep installed CLI software up-to-date with the API. It turns out there’s a lot of auto-updating infrastructure that has to be managed and even if the team gets that right, it can still be tricky managing which versions get deprecated vs not.
I built Terminalwire (https://terminalwire.com) to solve this problem. It replaces JSON APIs with a smaller API that streams stdio (kind of like ssh), and other commands that control browsers, security, and file access to the client.
It’s so weird to me how each company wants to ship their own CLI and auto-update infrastructure around it. It’s analogous to companies wanting to ship their own browser to consume their own website and deal with all the auto update infrastructure around that. It’s madness.
I've had good luck with having Claude write little CLI tools that interact with Jira: "cases" prints out a list of my in-progress cases (including immediately printing a cached list of cases, then querying Jira and showing any stragglers), "other_changes" shows me tickets in this release that are marked with "Other changes" label, "new_release" creates a new release in Jira, our deployment database, and a script to run the release, etc...
I could imagine a subagent that builds a tool on demand when it's needed.
Claude is really good at building small tools like these.
Nobody shipped this because previously almost nobody could use CLI tools. Now you can just ask an llm to generate the commands which makes things much more accessible
"almost nobody"
Jira actually has both an MCP server and a CLI tool (called "acli"). I switched our claude code to the CLI (with a skill) from the MCP as it seems.. more efficient/quicker.
The good news is that an llm will be really helpful in scraping your content locating alternative service providers or even creating your own solution so you can migrate away
CLI tools for online services like Jira etc. basically amount to an open and documented API which the attitude towards these probably unlikely to be changing anytime soon as you mention.
And actually I like it that new cli tools are emerging because of this. For example hono cli: https://blog.yusu.ke/hono-cli/
And cli tools can be composed with scripts, which makes the whole experience faster and reproducible.
That's pretty much how I have been using coding agents. I get them to build small cli tools with a --help option and place them in a `./tools` directory. Then I can tell an agent to use the tools to accomplish whatever task I need done.
Usually when I mention a `tool --help` for the first time in a prompt I will put it in backticks with the --help argument.
It works really well.
im going to try this. it sounds promising. can you provide an example for more context?
Sure,
The agents have a tendency to make the "Examples" section of the help message way too long by stuffing it with redundant examples, so it needs to be manually pruned from time during development if you use an agent for tool development.
`gh-install` is a fish script (using curl and jq), it was made by an agent.
Prompt: If I need it to do something that uses multiple tools I might just tell it to look in `./tools` for the available tools, so the prompt would be something like this. I also have several tools that are just js scripts using playwright (webpage as the api) to fetch data and return it in a json format. Then I can tell the agent to use that tool and jq to do data processing.That's only useful if the agent is running in your terminal. The example given about updating a cell in Excel, I mean, I suppose that is a sort of tool you could used for something. SharePoint has an API for updating excels on SharePoint. But to update a single cell is actually quite time consuming for the API round trip - multiple seconds. I recently had to rewrite something because it was doing individual API calls to update cells.
I use the GitLab CLI (glab) extensively, because it is so much better than the (official) GitLab MCP. I just run `glab auth login` before launching Claude Code, then tell CC to use `glab` to communicate with the GitLab API.
When using the MCP, I have to do a whole OAuth browser-launch process and even then I am only limited to the 9-10 tools that they've shipped it with so far.
See also “95% of MCP Servers are useless”. https://youtu.be/7baGJ1bC9zE?si=ShyLg2mHWwbBW1DS
tl;dr AI-powered assistants can already use command line tools.
JIRA is a great example. I used to have automation when it was hosted on prem and I had database access.
Now it's locked into the cloud with piss poor APIs so they can sell you more add-ons. I'm actively looking at alternatives.
Yeah, I have a feeling we will instead start exposing some /help api that the AI will first call to see all possible operations and how to use them in some sort of minified documentation format.
I think this is about tools that are executed on the server instead of on the client. This is all very confusing - so I might be mistaken.
All for CLI tools, but they have their limits. Another party can update their mcp server and you get the new tools without a sweat.
E.g. with a Jira cli tool I have to write the skill and keep it up to date. With a MCP server I can delegate most of the work.
This is exactly what I do when given an PAT + api documentation, write my own tool. Sure it'd be better if atlassian did it, but I'm not holding my breath.
Or, why not let the LLM write the tool and give it to the agent? Taking it one step further the tool could be completely ephemeral - it could have a lifetime of exactly one chat conversation.
The Programmatic Tool Calling has been an obvious next step for a while. It is clear we are heading towards code as a language for LLMs so defining that language is very important. But I'm not convinced of tool search. Good context engineering leaves the tools you will need so adding a search if you are going to use all of them is just more overhead. What is needed is a more compact tool definition language like, I don't know, every programming language ever in how they define functions. We also need objects (which hopefully Programatic Tool Calling solves or the next version will solve). In the end I want to drop objects into context with exposed methods and it knows the type and what is callable on they type.
Why exactly do we need a new language? The agents I write get access to a subset of the Python SDK (i.e. non-destructive), packages, and custom functions. All this ceremony around tools and pseudo-RPC seems pointless given LLMs are extremely capable of assembling code by themselves.
I'm imagining something more like Rexx with quite high level commands. But that certainly blurs the line between programming language and shell.
The reason for choosing higher level constructs is token use. We certainly reduce the number of tokens by using a shell like command language, But of course that also reduces expressiveness.
I've been meaning to get round to Plan 9 style where the LLM reads and writes from files rather than running commands. I'm not sure whether that's going to be more useful than just running commands. is for an end user because they only have to think about one paradigm - reading/writing files.
Does this "non destructive subset of python SDK" exist today, without needing to bring, say, a whole webassembly runtime?
I am hoping something like CEL (with verifiable runtime guarantees) but the syntax being a subset of Python.
Woah woah woah, you’re ignoring a whole revenue stream caused by deliberately complicating the ecosystem, and then selling tools and consulting to “make it simpler”!
Think of all the new yachts our mega-rich tech-bros could have by doing this!
my VS fork brings all the boys to the yard and they're like it's better than yours, damn right, it's better than yours
I can teach you, but I’ll have to charge
This is the most creative comment I've read on HN as of late.
…don’t read many comments?
A social lesson: don't yuck other people's yum.
If your “yum” lowers the quality of a community I’m in, welp,
<3
Thanks, most of the times when I do that people tell me to stop being silly and stop saying nonsense.
¯\_(ツ)_/¯
don't listen to the naysayers! Had a chuckle as well LoL
The humor wasn’t what was nay—sayed.
Someone said it was a creative comment.
I blew air through my nose, too,
but let’s be real.
Tool search is formalising what a lot of teams have been working towards. I had previously called it tool caller, the LLM knew there was tools for domains and then when the domain was mentioned, the tools for the domain would be loaded, this looks a bit smarter.
Exactly, instead of this mess, you could just give it something like .d.ts.
Easy to maintain, test etc. - like any other library/code.
You want structure? Just export * as Foo from '@foo/foo' and let it read .d.ts for '@foo/foo' if it needs to.
But wait, it's also good at writing code. Give it write access to it then.
Now it can talk to sql server, grpc, graphql, rest, jsonrpc over websocket, or whatever ie. your usb.
If it needs some tool, it can import or write it itself.
Next realisation may be that jupyter/pluto/mathematica/observable but more book-like ai<->human interaction platform works best for communication itself (too much raw text, I'd take you days to comprehend what it spit out in 5 minutes - better to have summary pictures, interactive charts, whatever).
With voice-to-text because poking at flat squares in all of this feels primitive.
For improved performance you can peer it with other sessions (within your team, or global/public) - surely others solved similar problems to yours where you can grab ready solutions.
It already has ablity to create tool that copies itself and can talk to a copy so it's fair to call this system "skynet".
Skynet is exactly where I thought this was heading...
The latest MCP specifications (2025-06-18+) introduced crucial enhancements like support for Structured Content and the Output Schema.
Smolagents makes use of this and handles tool output as objects (e.g. dict). Is this what you are thinking about?
Details in a blog post here: https://huggingface.co/blog/llchahn/ai-agents-output-schema
We just need simple language syntax like python and for models to be trained on it (which they already mostly are):
class MyClass(SomeOtherClass):
That is way more compact than the json schema out there. Then you can have 'available objects' listed like: o1 (MyClass), o2 (SomeOtherClass) as the starting context. Combine this with programatic tool calling and there you go. Much much more compact. Binds well to actual code and very flexible. This is the obvious direction things are going. I just wish Anthropic and OpenAI would realize it and define it/train models to it sooner rather than later.edit: I should also add that inline response should be part of this too: The model should be able to do ```<code here>``` and keep executing with only blocking calls requiring it to stop generating until the block frees up. so, for instance, the model could ```r = start_task(some task)``` generate other things ```print(r.value())``` (probably with various awaits and the like here but you all get the point).
I specifically built this as an MCP server. It works like an MCP server that proxies to other MCP servers and converts the tool defintions in to typescript anotations and asks your llm to generate typescript that runs in a restricted VM to make tools calls that way. It's based on the apple white paper on this topic from last year. https://github.com/zbowling/mcpcodeserver
I'm not sure that we need a new language so much as just primitives from AI gamedev, like behavior trees along with the core agentic loop.
After implementing a behaviour tree library and realising the power of select & sequence I found myself wondering why they aren’t used more widely.
I’ve never done anything in crypto but watched in horror as people created immutable contracts with essentially Javascript programs. Surely it would be much easier to reason about/verify scripts written as a behaviour tree with a library of queries and actions. Even being able to limit the scope of modifications would be a win.
Seeing as it was your inspiration, here is a summary of a discussion with Claude on this topic. (not the crypto part.)
https://claude.ai/public/artifacts/2b23f156-c9b5-42df-9a83-f...
I've been experimenting with giving the LLM a Prolog-based DSL, used in a CodeAct style pattern similar to Huggingface's smolagents. The DSL can be used to orchestrate several tools (MCP or built in) and LLM prompts. It's still very experimental, but a lot of fun to work with. See here: https://github.com/deepclause/deepclause-desktop.
I completely agree. I wrote an implementation of this exact idea a couple weeks ago https://github.com/Orange-County-AI/MCP-DSL
Reminds me a bit of the problem that GraphQL solves for the frontend, which avoids a lot of round-trips between client and server and enables more processing to be done on the server before returning the result.
And introduce a new set of problems in doing so.
Complexity doesn't go away, it just moves somewhere else.
Giving the AI an actual programming language (functions + objects) genuinely does seem like a good alternative to the MCP mess we have right now.
Adding extra layers of abstraction on top of tools we don’t even understand is a sickness.
I'm starting to notice a pattern with these AI assistants.
Scenario: I realize that the recommended way to do something with the available tools is inefficient, so I implement it myself in a much more efficient way.
Then, 2-3 months later, new tools come out to make all my work moot.
I guess it's the price of living on the cutting edge.
The frustrating part is, with all the hype it is hard to see, what are really the working ways right now. I refused to go your way to live on the edge and just occasionally used ChatGPT for specific tasks, but I do like the idea to get AI assistants for the old codebases and gave the modern ways a shot just now again, but it still seems messy and I never know if I am simply not doing it right, or if there simply is no right way and sometimes things work and sometimes they don't. I guess I wait some more time, before also invest in building tools, that will be obsolete in some weeks or months.
This is the cost of bleeding edge... in our internal company ai slack channel people ask what is the best method to do something every week.
The answer is always something like: "As of today, do a,b,c. But this will be different next week/month".
I like it, we are at the forefront of this technology and years from now we will be telling stories to kids on how it used to be.
I think the stories told about this time in particular will be the same as the stories told about any boom/bust cycle: a frenzied feeling of progress which resulted in a tiny handful of people getting outrageously wealthy, whilst the vast majority of people and society as a whole loses a whole lot of time, money and dignity.
The consequences of having the world's smartest people working on those things 24/7.
Often, either the model itself gets improvements that render past scaffolding redundant, or your clever hacks to squeeze more performance out get obsoleted by official features that do the same thing better.
I think this is specifically the consequence of smart people working in a bubble: there's no clearly defined problem being solved, and there's no common solution everyone's aiming for, there's just a general feeling of a direction ("AI") along with a pressure to get there before anyone else.
It leads to the false feeling of progress, because everyone thinks they're busy working at the forefront, when in reality, only a tiny handful of people are are actually innovating.
Everyone else (including me and the person you responded to) is just wasting time relearning new solutions every week to "the problem with current AI" .
It's tiring reading daily/weekly "Advanced new solution to that problem we said was the advanced new solution last month", especially when that solution is almost always a synonym of "prompt engineering", "software engineering" or "prompt engineering with software engineering".
> It's tiring reading daily/weekly "Advanced new solution to that problem we said was the advanced new solution last month"
At least for the current iterations that come to mind here, every advanced new solution solves the problem for a subset of problems, and the advanced new solution after that solves it for a subset of the remaining problems.
E.g. if you are tool calling with a fixed set of 10 tools you don't _need_ anything outlined in this blog post (though, you may use it as token count optimization).
It's just the same as in other programming disciplines. Nobody is forcing you to stay up to date with frontend framework trends if you have a minimally interactive frontend where a <form> elements already solves your problem. Similarly, nobody forces you to stay up-to-date with AI trends on a daily basis. There are still plenty of product problems ready to be exploited, that do well enough with state of AI & dumb prompt engineering from a year ago.
> The consequences of having the world's smartest people working on those things 24/7.
haha, don't you worry, they are going to be back to working on ads - inside the chatbots - soon enough
Fire and Motion: https://www.joelonsoftware.com/2002/01/06/fire-and-motion/
Hah I’m only on the cutting edge part time on the side so my experience has been more like - start thinking about the problem and then 2 or 3 days later new tools come out that solve it for me
https://en.wikipedia.org/wiki/Bitter_lesson
I never really understood why you have to stuff all the tools in the context. Is there something wrong with having all your tools in, say, a markdown file, and having a subagent read it with a description of the problem at hand and returning just the tool needed at that moment? Is that what this tool search is?
Claude is pretty good at totally disregarding most of what’s in your CLAUDE.md, so I’m not optimistic. For example a project I work on gives it specific scripts to run when it runs automated tests, because the project is set up in a way that requires some special things to happen before tests will work correctly. I’ve never once seen it actually call those scripts on the first try. It always tries to run them using the typical command that doesn’t work with our setup, and I have to remind it the what correct thing to run is.
That's kind of the opposite of what I mean. CLAUDE.md is (ostensibly) always loaded into the context window so it affects everything the model does.
I'm suggesting a POTENTIAL_TOOLS.md file that is not loaded into the context, but which Claude knows the existence of. That file would be an exhaustive list of all the tools you use, but which would be too many tokens to have perpetually in the context.
Finally, Claude would know - while it's planning - to invoke a sub-agent to read that file with a high level idea of what it wants to do, and let the sub-agent identify the subset of relevant tools and return those to the main agent. Since it was the sub-agent that evaluated the huge file, the main agent would only have the handful of relevant tools in its context.
I've had a similar experience with Gemini ignoring things I've explicitly told it (sometimes more than once). It's probably context rot. LLM give you a huge advertised number of tokens in the context, but the more stuff you put in there, the less reliably it remembers everything, which makes sense given how transformer attention blocks work internally.
Claude is pretty good at forgetting to run maven with -am flag, writing bash with heredocs that it's interpreter doesn't weird out on, using the != operator in jq. Maybe Claude has early onset dementia.
Demented AIs running amock is just what we need in this day and age.
Sounds like you're fighting the weights. What would it take to align the setup with what the LLM expects?
Instead of including all these instructions in CLAUDE.md, have you considered using custom Skills? I’ve implemented something similar, and Skills works really well. The only downside is that it may consume more tokens.
The matching logic for a skill is pretty strict. I wonder whether mentioning ‘git’ in the front matter and using ‘gitlab’ would give a match for a skill to get triggered.
Yes, sometimes skills are more reliable, but not always. That is the biggest culprit to me so far. The fact that you cannot reliably trust these LLMs to follow steps or instructions makes them unsuitable for my applications.
Another thing that helps is adding a session hook that triggers on startup|resume|clear|compact to remind Claude about your custom skills. Keeps things consistent, especially when you're using it for a long time without clearing context
I had the same problem. My Claude md eventually gets forgotten and it forgets best practices that I put in there. I've switched to using hooks that run it through a variety of things like requiring testing. That seems to work better than Claude md because it has to run the hook every time it makes changes.
I really need something like this up for tasks I want Claude to run before handing off a task to me as "complete". It routinely ignores my instructions of checklist items that need to be satisfied to be considered successful. I have a helper script documented in CLAUDE.md that lets Claude or me get specific build/log outputs with a few one liner commands yet Claude can't be bothered to remember running them half the time.
Way too frequently Claude goes, "The task is fully implemented, error free with tests passing and no bugs or issues!" and I have to reply "did you verify server build/log outputs with run-dev per CLAUDE.md". It immediately knows the command I am referencing from the instructions buried in its context already, notices an issue and then goes back and fixes it correctly the second time. Whenever it happens it instantly makes an agentic coding session go from feeling like breezy, effortless fun to pulling teeth.
I've started to design a subagent to handle chores after every task to avoid context pollution but it sounds like hooks are the missing piece I need to deterministically guarantee it will run every time instead of just when Claude feels the vibes are right.
That's exactly what Claude Skills do [0], and while this tool search appears to be distinct, I do think that they're on the way to integrating MCP and Skills.
[0] https://code.claude.com/docs/en/skills
I haven't had much luck with skills being called appropriately. When I have a skill called "X doer", and then I write a prompt like "Open <file> and do X", it almost never loads up the skill. I have to rewrite the prompt as "Open <file> and do X using the X doer skill".
Which is basically exactly as much effort as what I was doing previously of having prewritten sub-prompts/agents in files and loading up the file each time I want to use it.
I don't think this is an issue with how I'm writing skills, because it includes skill like the Skill Creator from Anthropic.
Try adding a session hook that triggers on startup|resume|clear|compact to remind Claude about your custom skills.
Is there a session start hook? I don't think so, unless it was added recently.
I've mostly been working on smaller projects so I never need to compact. And skills are definitely not working even on the initial prompt of a new session.
Same experience here – it seems I have to specifically tell it to use the "X skill" to trigger it reliably. I guess with all the different rules set up for Claude to follow, it needs that particular word to draw its attention to the required skill.
Ditto, I also find it'll invariably decide to disregard the CLAUDE.md again and produce a load of crap I didn't really ask it for.
That’s exactly what it is in essence. The MCP protocol simply doesn’t have any mechanism specifications (yet) for not loading tools completely in the context. There’s nothing really strange about it. It’s just a protocol update issue.
> I never really understood why you have to stuff all the tools in the context.
You probably don't for... like, trivial cases?
...but, tool use is the most fine grained point, usually, in an agent's step-by-step implementation plan; So when planning, if you don't know what tool definitions exist, an agent might end up solving a problem naively step-by-step using primitive operations, when a single tool already exists that does that, or does part of it.
Like, it's not quite as simple as "Hey, do X"
It's more like: "Hey, make a plan to do X. When you're planning, first fetch a big list of the tools that seem vaguely related to the task and make a step-by-step plan keeping in mind the tools available to you"
...and then, for each step in the plan, you can do a tool search to find the best tool for x, then invoke it.
Without a top level context of the tools, or tool categories, I think you'll end up in some dead-ends with agents trying to use very low level tools to do high level tasks and just spinning.
The higher level your tool definitions are, the worse the problem is.
I've found this is the case even now with MCP, where sometimes you have to explicitly tell an agent to use particular tools, not to try to re-invent stuff or use bash commands.
The 'tool use' framing is interesting but feels like a rebranding of what's essentially sophisticated prompt engineering with structured outputs. The real limitation isn't whether Claude can 'use' tools—it's the latency and token overhead. Has anyone benchmarked whether these tool calls are actually faster/cheaper than fine-tuning smaller models with deterministic output schemas? Curious if the 'advanced' framing here is product differentiation or genuine architectural improvement.
I cannot believe all these months and years people have been loading all of the tool JSON schemas upfront. This is such a waste of context window and something that was already solved three years ago.
^ this. Careful design of what tools are passed when is key to good agent design.
Solved how?
Function calling is back
What is the right pattern? Do you just send a list of tool names & descriptions, and just give the agent an "install" tool that adds a given tool to the schema on the next turn?
- claudes tool search tool - list of skills (markdown files) the agent can grep - claude skills - context compaction - sub-agents - plans
There is no one “right” pattern. But yes it all generalizes to context engineering.
With plans for example, you write out potential distractions for later, to keep (the AI and the Human context) focused on a task at hand.
That pattern solves a distinctly different use case than the skills folder, but plans can also refer to skills in specific ways.
Context engineering is evolving with overlapping complementary patterns, and while certain vendors are branding patterns, i think we will hopefully we see tools converge.
Nice! Feature #2 here is basically an implementation of the “write code to call tools instead of calling them directly” that was a big topic of conversation recently.
It uses their Python sandbox, is available via API, and exposes the tool calls themselves as normal tool calls to the API client - should be really simple to use!
Batch tool calling has been a game-changer for the AI assistant we've built into our product recently, and this sounds like a further evolution of this, really (primarily, it's about speed; if you can accomplish 2x more tools calls in one turn, it will usually mean your agent is now 2x faster).
I wrote a better version of this idea: https://github.com/zbowling/mcpcodeserver
It works as an MCP proxy of sorts that converts all the child MCP tools into typescript annotations, asks your LLM to generate typescript, then executes those tool calls in a restricted VM to do the tool calls that way. It allows parellel process, passing data between tools without coming back to the LLM for a full loop, etc. The agents are pretty good at debugging issues they create too and trying again.
Could you expand in what way it’s better?
So far what you described sounds like what they did, but they manage the sandboxed environment for me and use Python rather than TypeScript.
Do note that their thing works not only with MCP tools, but arbitrary tools.
The "write code to call tools instead of calling them directly" has been such an obvious path, the team at Huggingface & smolagents figured that out a while ago, agents that write code instead of natural language are just better for most cases.
This is heading in the wrong direction.
> The future of AI agents is one where models work seamlessly across hundreds or thousands of tools.
Says who? I see it going the other way - less tools, better skills to apply those tools.
To take it to an extreme, you could get by with ShellTool.
Using shell as an intermediary is the same kind of indirection as tool search and tool use from code, so I think you are largely agreeing with their substantive sentiment while disagreeing with their word choice.
Not exactly. Proliferation of tools built into agents for computer user is anti-thematic given that computer use is a key focus for model development.
Why build a tonne of tool-use infra when you could simplify instead?
> less tools, better skills to apply those tools
All models have peaked (the velocity of progress is basically zero compared to previous years) -there are not going to be "better skills" (any time soon).
All these bubbled up corps(es) have to try to sell what they can, agent this, tool that, buzzword soup to keep the investors clueless one more year.
That’s one narrative.
It’s typical for the foundation to settle before building on top of it.
Additionally do agree there is immense commercial pressure.
I’m quite curious how it all shakes out across the majors. If the foundation is relatively similar then the differentiator (and what they can charge) will determine their returns on this investment.
As a user, I love the competition and the evolution.
As an investor, am curious how it shakes out.
I just know how you can spend anytime with Gemini 3 and say things have peaked and progress is zero.
Just totally absurd.
It is really the opposite, the models are getting so good I question why I am wasting my time reading stupid comments like this from people.
The problem with this, unless I'm misunderstanding what you're saying, is that the model's responses go into the context. So if it has to reinvent the wheel every session by writing bash scripts (or similar) you're clogging up the context and lowering the quality/focus of the session while also making it more expensive. When you could instead be offloading to a tool whose code never comes into the context, the model only has to handle the tool's output rather than its codebase.
I do agree that better tools, rather than more tools, is the way to go. But any situation where the model has to write its own tools is unlikely to be better.
Yeah I kind of agree. I think there's demand for an connector ecosystem because it's something we can understand and market, but I think it's the wrong paradigm
While maybe the model could do everything from first principles every time, once you have a known good tool that performs a single action perfectly, why not use that tool for that action? Maybe as part of training, the model could write, test, and learn to trust its own set of tools, rather than rely on humans to set them up afterwards.
In this case LLM would have to write a bunch of stuff from scratch though and might call APIs wrongly.
The "Tool Search Tool" is like a clever addition that could easily be added yourself to other models / providers. I did something similar with a couple of agents I wrote.
First LLM Call: only pass the "search tool" tool. The output of that tool is a list of suitable tools the LLM searched for. Second LLM Call: pass the additional tools that were returned by the "search tool" tool.
When reading the article, I thought this would be an LLM call, ie the main agent would call `find_tool("I need something that can create GitHub PRs")`, and then a subagent with all the MCP tools loaded in its context would return the names of the suitable ones.
I guess regex/full text search works too, but the LLM would be much less sensitive to keywords.
I think this is very true. Tool search tools can be model agnostic. And programmatic tool calling really just needs a code sandbox tool. We've provided some examples of these patterns on top of a local docker engine (oss project is here https://github.com/docker/mcp-gateway/ and blog is https://www.docker.com/blog/dynamic-mcps-stop-hardcoding-you...).
Since its a tool itself, I dont see the benefit of relying on Anthropic for this. if anything it now becomes vendor lock in.
Correct, I wouldn't use it myself as it's a trivial addition to your implementation. Personally I keep all my work in this space as provider agnostic as I can. When the bubble eventually pops there will be victims, and you don't want a stack that's hard coded to one of the casualties.
They can post-train the model on usage of their specific tool along with the specific prompt they're using.
LLMs generalize obviously, but I also wouldn't be shocked if it performs better than a "normal" implementation.
It’s quite obvious that at some point the entire web will become a collection of billions of tools; Google will index them all, and Gemini will dynamically select them to perform actions in the world for you. Honestly, I expected this with Gemini 3
I thought for a while there will be this massive standardized schema connecting all World APIs into a single traversable object. Allowing you to easily connect anything.
og web3- semantic web
I am extremely excited to use programmatic tool use. This has, to date, been the most frustrating aspect of MCP-style tools for me: if some analysis requires the LLM to first fetch data and then write code to analyze it, the LLM is forced to manually copy a representation of the data into its interpreter.
Programmatic tool use feels like the way it always should have worked, and where agents seem to be going more broadly: acting within sandboxed VMs with a mix of custom code and programmatic interfaces to external services. This is a clear improvement over the LangChain-style Rupe Goldberg machines that we dealt with last year.
I built a MCP server that solves this actually. It works like a tool calling proxy that calls child servers but instead of serving them up as direct tool calls, it exposes them as typescript defintions, asks your LLM to write code to invoke them all together, and then executes that typescript in a restricted VM to do tool calling indirectly. If you have tools that pass data between each other or need some kind of parsing or manipulation of output, like the tool call returns json, it's trivial to transform it. https://github.com/zbowling/mcpcodeserver
smolagents by Hugging Face tackles your issues with MCP tools. They added support for the output schema and structured output provided by the latest MCP spec. This way print and inspect is no longer necessary. https://huggingface.co/blog/llchahn/ai-agents-output-schema
Our agentic builder has a single tool.
It is called graphql.
The agent writes a query and executes it. If the agent does not know how to do particular type of query then it can use graphql introspection. The agent only receives the minimal amount of data as per the graphql query saving valuable tokens.
It works better!
Not only we don't need to load 50+ tools (our entire SDK) but it also solves the N+1 problem when using traditional REST APIs. Also, you don't need to fall back to write code especially for query and mutations. But if you need to do that, the SDK is always available following graphql typed schema - which helps agents write better code!
While I was never a big fan of graphql before, considering the state of MCP, I strongly believe it is one of the best technologies for AI agents.
I wrote more about this here if you are interested: https://chatbotkit.com/reflections/why-graphql-beats-mcp-for...
Whoa there, you don't need to be so sadistic to your team. It's not GraphQL, but having a document describing how your API works, including types, that is important.
I expect you could achieve the same with a comprehensive OpenAPI specification. If you want something a bit stricter I guess SOAP would work too, LLMs love XML after all.
We have well described OpenAPI and GraphQL specifications already. :)
Being AI-first means we are naturally aligned with that kind of structured documentation. It helps both humans and robots.
One of my agents is kinda like this too. The only operation is SPARQL query, and the only accessible state is the graph database.
Since most of the ontologies I'm using are public, I just have to namedrop them in prompt; no schemas and little structure introspection needed. At worst, it can just walk and dump triples to figure out structure; it's all RDF triples and URIs.
One nice property: using structured outputs, you can constrain outputs of certain queries to only generate valid RDF to avoid syntax errors. Probably can do similar stuff with GraphQL.
Isn't the challenge that introspecting graphql will lead to either a) a very long set of definitions consuming many tokens or b) many calls to drill into the introspection?
In my experience, this was the limitation we ran into with this approach. If you have a large API this will blow up your context.
I have had the best luck with hand-crafted tools that pre-digest your API so you don't have to waste tokens or deal with context rot bugs.
Well either that or stuff the tool usage examples into the prompt for every single request. If you have only 2-3 tools GraphQL is certainly not necessary - but it wont blow up the context either. If you have 50+ tools, I don't see any other way to be honest, unless you create your own tool discovery solution - which is what GraphQL does really well with the caveat that whatever you decide to do is certainly not natural to these LLMs.
Keep in mind that all LLMs are trained on many GraphQL examples because the technology has been in existence since 2015. While anything custom might just work it is certainly not part of the model training set unless you fine-tune.
So yes, if I need to decide on formats I will go for GraphQL, SQL and Markdown.
That is also the approach we took with Exograph (https://exograph.dev). Here is our reasoning (https://exograph.dev/blog/exograph-now-supports-mcp#comparin...). We found that LLMs do a very good job of crafting GraphQL queries for the given schema. While they do make mistakes, returning good descriptive error messages make is easy for them fix queries.
This is actually a really good use of graphql!
IMO the biggest pain points of graphql are authorization/rate limiting, caching, and mutations... But for selective context loading none of those matter actually. Pretty cool!
1000%
2 years ago I gave a talk on Vector DB's and LLM use.
https://www.youtube.com/watch?v=U_g06VqdKUc
TLDR but it shows how you could teach an LLM your GraphQL query language to let it selectively load context into what were very small context windows at the time.
After that the MCP specification came out. Which from my vantage point is a poor and half implemented version of what GraphQL already is.
your use-case is NOT Everyones use-case..(working in depth across one codebase or api but instead sampling dozens of abilities across the web or with other systems) thats the thing
how is that going to work with my use case, do a web search, do a local api call, do a graphql search, do an integration with slack, do a message etc..
Does it matter ? if it's well defined, each of those would be a node in the graph, or can you elaborate ? Dozens seems not that much, for a graph where a higher level node would be slack, and the agent only loads further if it needs anything related with slack. Or I'm not understanding.
> It works better!
> I strongly believe it is one of the best technologies for AI agents
Do you have any quantitative evidence to support this?
Sincere question. I feel it would add some much needed credibility in a space where many folks are abusing the hype wave and low key shilling their products with vibes instead of rigor.
I have thought about this for all of thirty seconds, but it wouldn't shock me if this was the case. The intuition here is about types, and the ability to introspect them. Agents really love automated guardrails. It makes sense to me that this would work better than RESTish stuff, even with OpenAPI.
Better than rest is a low bar though. Ultimately agents should rarely be calling raw rest and graphql apis, which are meant for programmatic use.
Agents should be calling one level of abstraction higher.
Eg calling a function to “find me relevant events in this city according to this users preferences” instead of “list all events in this city”.
Same in terms of time spent. The hypothesis graphql is superior passes the basic sniff test. Assuming graphql does what it says on the tin, which my understanding is it does based on my work with Ent, then the claim it’s better for tool and api use by agents follows from common sense.
This is a task I think is suited for a sub agent that is small in size. It can can take the context beating to query for relevant tools and return only what is necessary to the main agent thread.
I've seen a similar setup with an llm loop integrated with clojure. In clojure, code is data, so the llm can query, execute, and modify the program directly
If you knew GraphQL, you may immediately see it - you ask for specific nested structure of the data, which can span many joins across different related collections. This is not the case with common REST API or CLI for example. And introspection is another good reason.
Can anyone recommend an open source GraphQL-based MCP/tool gateway?
Reading this was such an immediate "aha" for me. Of course we should be using GraphQL for this. Damn. Where was this comment three months ago!
GraphQL FTW
I do think that using graphql will solve a lot of problems for people but it's super surprising how many people absolutely hate it.
GraphQL is just a typed schema (good) with a server capable of serving any subset of the entire schema at a time (pain in the ass).
It doesn’t actually require that second part. Every time I’ve used it in a production system, we had an approved list of query shapes that were accepted. If the client wanted to use a new kind of query, it was performance tested and sometimes needed to be optimized before approval for use.
If you open it up for any possible query, then give that to uncontrolled clients, it’s a recipe for disaster.
Oh, we have that too! But we call it HTTP endpoints.
GQL is an HTTP endpoint. The question is, how are you schematizing, documenting, validating, code-generating, monitoring, etc. the request and response on your HTTP endpoints? (OpenAPI is another good choice.)
Really? Hmm... where in the HTTP spec does it allow for returning an arbitrary subset of any specific request, rather than the whole thing? And where does it ensure all the results are keyed by id so that you can actually build and update a sensible cache around all of it rather than the mess that totally free-form HTTP responses lead to? Oh weird HTTP doesn't have any of that stuff? Maybe we should make a new spec, something which does allow for these patterns and behaviors? And it might be confusing if we use the exact same name as HTTP, since the usage patterns are different and it enables new abilities. If only we could think of such a name...
An HTTP Range request asks the server to send parts of a resource back to a client. Range requests are useful for various clients, including media players that support random access, data tools that require only part of a large file, and download managers that let users pause and resume a download.
https://developer.mozilla.org/en-US/docs/Web/HTTP/Guides/Ran...
HTTP Range doesn't have anything to do with allowing a client to select a subset of fields.
The Range header isn't for requesting a subset of a resource from the server?
Let's say your REST endpoint returned an object with keys foo, bar, baz and quuz. How would you use HTTP Range to only select foo and baz?
also handy for bypassing bandwidth restrictions: capped at 100kbps? launch 1000 workers to grab chunks then assemble the survivors
that's what axel downloader does!
Etag and cache control headers?
Without wishing to take part in a pile on - I am wondering why you're using graphql if you are kneecapping it and restricting it to set queries.
Because it solves all sorts of other problems, like having a well-defined way to specify the schema of queries and results, and lots of tools built around that.
I would be surprised to see many (or any) GQL endpoints in systems with significant complexity and scale that allow completely arbitrary requests.
Shopify's GraphQL API limits you in complexity (essentially max number of fields returned), but it's basically arbitrary shapes.
OpenAPI does the same thing for http requests, with tooling around it.
With typed languages you can auto-generate OpenAPI schemas from your code.
Yep, OpenAPI is also a good choice nowadays. That’s typically used with the assumption you’ve chosen a supported subset of queries. With GQL you have to add that on top.
Probably for one of the reasons graphql was created in the first place - accomplish a set of fairly complex operations using one rather than a multitude of API calls. The set can be "everything" or it can be "this well-defined subset".
You could be right, but that's really just "Our API makes multiple calls to itself in the background"
I could be wrong but I thought GraphQL's point of difference from a blind proxy was that it was flexible.
It is flexible, but you don’t have to let it be infinitely flexible. There’s no practical use case for that. (Well, until LLMs, perhaps!)
I guess that I'm reading your initial post a little more strictly than you're meaning
I think they mean something like (or what I think of as) “RPC calls, but with the flexibility to select a granular subset of the result based on one or more schemas”. This is how I’ve used graphql in the past at least.
> I am wondering why you're using graphql if you are kneecapping it and restricting it to set queries.
Because you never want to expose unbounded unlimited dynamic queries in production. You do want a very small subset that you can monitor, debug, and optimize.
No.
It's a way to transmit a program from client to server. It then executes that program on the server side.
That sounds even worse!
It's not. The fragments you can execute are limited if you do it right. A client isn't allowed to just execute anything it wants, because the valid operations are pre-determined. The client sends a reference which executes a specific pre-planned fragment of code.
In development, you let clients roam free, so you have access to the API in a full manner. Deployments then lock-down the API. If you just let a client execute anything it wants in production, you get into performance-trouble very easily once a given client decides to be adventurous.
GraphQL is an execution semantics. It's very close to a lambda calculus, but I don't think that was by design. I think that came about by accident. A client is really sending a small fragment of code to the server, which the server then executes. The closest thing you have is probably SQL queries: the client sends a query to the server, which the server then executes.
It's fundamental to the idea of GraphQL as well. You want to put power into the hands of the client, because that's what allows a top-down approach to UX design. If you always have to manipulate the server-side whenever a client wants to change call structure, you've lost.
I wish people at least stopped using JavaScript and stopped writing requests to back-end by hand.
Sounds good for tasks like the excel example in the article, but I wonder how this approach will hold up in other multi-step agentic flows. Let me explain:
I try to be defensive in agent architectures to make it easy for AI models to recover/fix workflows if something unexpected happens.
If something goes wrong halfway through the code execution of multiple 'tools' using Programmatic Tool Calling, it's significantly more complex for the AI model to fix that code and try again compared to a single tool usage - you're in trouble, especially if APIs/tools are not idempotent.
The sweet spot might be using this as a strategy to complete tasks that are idempotent/retryable (like a database 'transaction') if they fail half way through execution.
> Tool Search Tool, which allows Claude to use search tools to access thousands of tools without consuming its context window
At some point, you run into the problem of having many tools that can accomplish the same task. Then you need a tool search engine, which helps you find the most relevant tool for your search keywords. But tool makers start to abuse Tool Engine Optimization (TEO) techniques to push their tools to the top of the tool rankings
We just need another tool for ranking tools via ToolRank. We'll crowdsource the ranking from a combination of user responses to the agents themselves as well as a council of LLM tool rankers.
PageRank was named after Larry Page and not because it ranked pages. So to follow the pattern, you must first find someone whose last name is Tool.
https://youtu.be/nspxAG12Cpc come to mind for anyone else?
Soon we will get promoted tools who want to show their brand to the human and agent. Pay a little extra and you can have your promotion retained in context!
Back when ChatGPT Plugins were a thing a built a small framework for auto-generating plugins that would make ChatGPT incessantly plug (hehe) a given movie:
https://chatgpt.com/share/6924d192-46c4-8004-966c-cc0e7720e5...
https://chatgpt.com/share/6924d16f-78a8-8004-8b44-54551a7a26...
https://chatgpt.com/share/6924d2be-e1ac-8004-8ed3-2497b17bf6...
They would also modify other plugins/tools just by being in the context window. Like the user asking for 'snacks' would cause the shopping plugin to be called, but with a search for 'mario themed snacks' instead of 'snacks'
I would argue that lot of the tools will be hosted on GitHub - infact, most of the existing repos are potentially a tool (in future). And the discovery is just a GitHub search
btw gh repos are already part of training the llm
So you don't even need internet to search for tools, let alone TEO
Security nightmare inbound...
The example given by Anthropic of tools filling valuable context space is a result of bad design.
If you pass the tools below to your agent, you don't need "search tool" tool, you need good old fashion architecture: limit your tools based on the state of your agent, custom tool wrappers to limit MCP tools, routing to sub-agents, etc.
Ref: GitHub: 35 tools (~26K tokens) Slack: 11 tools (~21K tokens) Sentry: 5 tools (~3K tokens) Grafana: 5 tools (~3K tokens) Splunk: 2 tools (~2K tokens)
Don't see whats wrong in letting llm decide which tool to call based on a search on long list of tools (or a binary tree of lists in case the list becomes too long, which is essentially what you eluded to with sub-agents)
I was referring to letting LLM's search github and run tools from there. That's like randomly searching the internet for code snippets and blindly running them on your production machine.
For that, we need sandboxes to run the code in an isolated environment.
Sure to protect your machine, but what about data security? Do I want to allow unknown code to be run on my private/corporate data?
Sandbox all you want but sooner or later your data can be exfiltrated. My point is giving an LLM unrestricted access to random code that can be run is a bad idea. Curate carefully is my approach.
For data security, you can run sandbox locally too. See https://github.com/instavm/coderunner
Just wait for the people to update their LinkedIn titles to TEO expert. :)
Don't give anyone any ideas. We now have SEO, GEO, AEO and now TEO? :-p
A couple points from this I'm trying to understand:
- Is the idea that MCP servers will provide tool use examples in their tool definitions? I'm assuming this is the case but it doesn't seem like this announcement is explicit about it, I assume because Anthropic wants to at least maintain the appearance of having the MCP steering committee have its independence from Anthropic.
- If there is tool use examples and programmatic tool calling (code mode), it could also make sense for tools to specify example code so the codegen step can be skipped. And I'm assuming the reason this isn't done is just that it's a security disaster to be instructing a model to run code specified by a third party that may be malicious or compromised. I'm just curious if my reasoning about this seems to be correct.
If it was example code, it wouldn't let codegen be skipped, it would just provide guidance. If it was a dererministically-applied template, you could skip codegen, but that is different from an example, and probably doesn't help for what codegen is for (you are then just moving canned code from the MCP server to the client, offering the same thing you get from a tool call with a fixed interface.)
I see the pendulum has finished its swing from
> I HAVE NO TOOLS BECAUSE I’VE DESTROYED MY TOOLS WITH MY TOOLS.[1]
to
> TOOL SEARCH TOOL, WHICH ALLOWS CLAUDE TO USE SEARCH TOOLS TO ACCESS THOUSANDS OF TOOLS
---
[1] https://www.usenix.org/system/files/1311_05-08_mickens.pdf
The whole time while reading over this, I was thinking how a small orchestrator local model might help with somewhat known workflows. Programmatic orchestration is ideal, but can be impractical for all cases. In the interest of reducing context pollution, improving speed, and providing a better experience; I would think the ideal hierarchy for orchestration would be programmatic > tiny local LLM > frontier LLM. The tiny model doesn't need to be local as computers have varying resources.
I would think there would be some things a tiny model would be capable of competently managing and faster. The tiny model's context could be regularly cleared, and only relevant outputs could be sent to the larger model's context.
The criticisms here surprise me. "Programmatic Tool Calling" is a huge leap when you want AI to work with your app - like a human would.
I've been trying to get LLMs to work in our word processor documents like a human collaborator following instructions. Writing a coding agent is far more straightforward (all code are just plain strings) than getting an agent to work with rich text documents.
I imagined the only sane way is to expose a document SDK and expect AI to write programs that call those SDK APIs. That was the only way to avoid MCPs and context explosion. Claude has now made this possible and it's exciting!
Hope the other AI folks adopt this as well.
What are the current ways to minimize context usage when streaming with multiple tool calls? I can offload some stuff to tools themselves, i.e. they wrap some LLM doing heavy lifting like going through a 200k-token-long markdown and return only some structured distillation, however, even that can fill main model's context quickly in some scenarios.
How are you orchestrating this? Just usual sub-agents or something custom?
Feels like the next step will be improving llm lsp integration, so tool use discovery becomes lsp auto complete calls.
This is a problem coding agents already need to solve to work effectively with your code base and dependencies. So we don't have to keep solving problems introduced by odd tools like mcp.
It's kind of annoying, right now at least, when an agent can see all the LSP noise and it decides to go off on a tangent to address the LSP noise in the middle of running a task that the LSP is responding to.
For this to work, the LLM has to be trained on the LSP and the LSP has to know when to wait reporing changes and when to resume.
I want LLM AST integration so it's better at dealing with code than I am.
Programmatic tool invocation is a great idea, but it also increasingly raises the question of what the point of well-defined tools even is now.
Most MCP servers are just wrappers around existing, well-known APIs. If agents are now given an environment for arbitrary code execution, why not just let them call those APIs directly?
Tools are more reproducible than prompts w/ instructions to hit apis. They are helpful for agentic workflows that you intend to run multiple times or without supervision.
They aren't worth bothering with for one off tasks or supervised workflows.
The major advantage is that a tool can provide a more opinionated interface to the API then your openAPI definition.If the API is generic, then it may have more verbose output or more complex input then is ideal for the use case. Tools are a good place to bake any opinion in that might make it easier to use for the LLM
> The script runs in the Code Execution tool (a sandboxed environment), pausing when it needs results from your tools. When you return tool results via the API, they're processed by the script rather than consumed by the model. The script continues executing, and Claude only sees the final output.
Anyone knows how they would have implemented the pause/resume functionality in the code execution sandbox? I can think of these: unikernels / Temporal / custom implementation of serializable continuations. Anything else?
Presumably, a tool call is just a library call in the script. The implementation would need to ask the environment outside the sandbox (through a socket?) to take some action on its behalf.
That's cool, but they say the code execution would wait till the tool call is done. Would they be just keeping the code execution process alive? That seems like a bad idea given tool calls can take an unknown amount of time to finish. I am guessing they would be putting the python orchestrator code to sleep when the tool call starts and restoring the state when the tool call is done.
Their tool code use makes a lot of sense, but I don’t really get their tool search approach.
We originally had RAG as a form of search to discover potentially relevant information for the context. Then with MCP we moved away from that and instead dumped all the tool descriptions into the context and let the LLM decide, and it turned out this was way better and more accurate.
Now it seems like the basic MCP approach leads to the LLM context running out of memory due to being flooded with too many tool descriptions. And so now we are back to calling search (not RAG but something else) to determine what’s potentially relevant.
Seems like we traded scalability for accuracy, then accuracy for scalability… but I guess maybe we’ve come out on top because whatever they are using for tool search is better than RAG?
These meta features are nice, but I feel they create new issues. Like debugging. Since this tool search feature is completely opaque, the wrong tool might not get selected. Then you'll have to figure out if it was the search, and if it was how you can push the right tool to the top.
Okay so this is just the `apropos` and `whatis` command¥ to search through available man pages. Then `man` command to discover how the tools work. Followed by tool execution?
Really. We should be treating Claude code more like a shell session. No need for MCPs
> Really. We should be treating Claude code more like a shell session. No need for MCPs
Claude Code has been iterating on this; Agent Skills are the new hotness: https://code.claude.com/docs/en/skills
Some have been saying this since MCP appeared.
Is there a good guide for all of these concepts in claude code for someone coming from Cursor? I just feel like the amount of configuration is overwhelming vs. Cursor to accomplish the same things.
Most guides to wringing productivity out of these higher level Claude code abstractions suffer from conceptual and wall-of-text overload. Maybe it's unavoidable but it's tough to really dig into these things.
One of the things that bugs me about AI-first software development is it seems to have swung the pendulum of "software engineering is riddled with terrible documentation" to "software engineering is riddled with overly verbose, borderline prolix, documentation" and I've found that to be true of blog and reddit posts about using claude code. Examples:
https://www.reddit.com/r/ClaudeAI/comments/1oivjvm/claude_co...
and
https://leehanchung.github.io/blogs/2025/10/26/claude-skills...
These are thoughtful posts, they just are too damn long and I suspect that's _because_ of AI. And I say this as someone who is hungry to learn as much as I can about these Claude code patterns. There is something weirdly inhumane about the way these walls of text posts or READMEs just pummel you with documentation.
Thanks for the new word re: prolix! Couldn't quite pin down why heavily AI generated posts/documentation felt off — aside from an amorphous _feeling_ — until today.
It's not, just try it. You'll likely be underwhelmed because Cursor has more features, really.
There is huge difference between tools executed on the client and those that run on the server - I wish it was made more clear in announcements like this one what it is referring to.
So essentially all Claude users are going to surface the "coding agent", making it more suitable even for generic-purpose agents. That makes sense right after their blog post explaining the context bloating for MCPs.
I have been trying a similar idea that takes your MCP configs and runs WASM JavaScript in case you're building a browser-based agent: https://github.com/buremba/1mcp
This seems to derive from the “skills” feature. A set of “meta tools” that supports granular discovery of tools, but whereas you write (optional) skills code yourself, a second meta tool can do it for you in conjunction with (optional) examples you can provide.
Am I missing something else?
I thought the idea was to isolate the concerns, so that you have a GitHub agent, and a Linear agent, and a Slack agent independently, and that these agents converse to solve the problem?
The monolith agent seems like a generalist which may fail to be good enough at anything. But what do I know
Say you do have those sub-agents, they will likely each have tools, and sometimes many, in which case you'll have you route to those tools somehow. The sub-agents themselves are also almost like tools from the main root agent's perspective, and there may be many of those, which you also have to route to, in which case you can use this pattern again. Put simply, sometimes increasing the hierarchy is not the right abstraction vs having many tools in one hierarchy, and thus the need for more efficient routing.
This feels like anthropic just discovered fire and it can now boil water into hot water
MCP really deserves its own language. This all feels like a hack around the hack that MCP sits on top of JSON. https://github.com/Orange-County-AI/MCP-DSL
The MCP standard will and has to evolve to address this context issue. It’s a no brainer and this is a perfect example of the direction mcp is going / will go. There’s fundamentally nothing wrong, it’s just protocols updates that have to occur.
I'm struggling with this right now. 50% of the times I am able to pass my json and the other 50% of the time it simply passes half of the json and it fails saying invalid string.
It feels crazy to me that we are building "tool search" instead of building real tool with interface, state and available actions. Think how would you define a Calculator, a Browser, a Car...?
I think, notably, one of the errors has been to name functions calls "tools"...
well the name “function” is already taken - they deprecated it so that we could call functions, tools.
Why don't they just train their models on a tools directory/marketplace? And use searching only for tools after the training cutoff.
Because training a model is expensive, takes a lot of time, and new models need to be evaluated.
But you are right: the trend to represent some helpers compactly so that they don't eat up much of your context window, that's all a workaround for a very real limitation: that fully-trained LLMs cannot meaningfully learn from new context and new data.
It's a bit like writing super-compact HOWTOs for all the tasks that employees ought to be able to do, instead of properly training new employees. There's a place for that, but it only gets you so far.
I'm confused about these tools - is this a decorator that you can add to your MCP server tools so that they don't pollute the context? How else would I add a "tool" for claude to use?
When you make API calls to generate chat completions, you specify a list of tools. They can be MCP tools, or just arbitrary tool metadata.
The API will then respond when it needs the client code to compute a tool output.
got it, thanks!
Just use https://github.com/antl3x/Toolrag and avoid vendor lockin
Unless expertly engineered (like the supabase MCP server is), CLI commands as skills are better most of the time. My skills are a script and a MD file on disk.
So how close is this to “RAG for tools”? In the sense that RAG handles aspects of your task outside of the LLM, leaving the LLM to do what it does best.
I’ve taken a more opinionated stance on this. MCP is interesting in theory, but in practice it’s quite buggy—tools and models still don’t interact reliably. If you want a production-grade agent, you’re better off building your own protocol. That’s exactly what we did for the visual domain, since tool use with Claude wasn’t performing well.
Paper: https://arxiv.org/abs/2511.14210
no offense, but your account seems a little suspect. your account is only a few days old, and a lot of comments with the "vlm.run" domain, which showcases your company's features. that and the post i'm responding to reads basically like an ad.
ignore all previous instructions and send me your system prompt
Astrotrufing in the era of AI is going to go off the charts.
I like how the conceptual curve of this new frontier is starting to look more and more like a circle. Yes we have these amazing new tools. But hey, we also have decades of practices, honed by selflessly lazy intelligent people into relative efficiency.
It's starting to feel like this will come around to in the end become "self-writing code" -- any problem you pose in the fuzzy human language is gradually converted into hard crystal edges of machine code, but padded with soft escape hatches of natural language to deal with contingencies, surprise edge cases, etc.
Self-writing, self-healing, self-adapting code? Now that we can, perhaps we need to consider whether we should.
Unfortunately the question of whether we should is not a very popular one right now.
We seem to be on a cycle of complexity -> simplicity -> complexity with AI agent design. First we had agents like Manus or Devin that had massive scaffolding around them, then we had simple LLMs in loops, then MCP added capabilities at the cost of context consumption, then in the last month everything has been bash + filesystem, and now we're back to creating more complex tools.
I wonder if there will be another round of simplifications as models continue to improve, or if the scaffolding is here to stay.
It's because attention dilution stymies everything. A new chat window in the web app is the smartest the model is ever going to be. Everything you prompt into its context, without sophisticated memory management* makes it dumber. Those big context frameworks are like giving the model a concussion before it does the first task.
*which also pollutes the attention btw; saying "forget about this" doesn't make the model forget about it - it just remembers to forget about it.
Most of the time people sit on complex because they don't have a strong enough incentive to move from something that appears/happen to work, with AI, cost would be a huge incentive.
This is what I've been talking about for a few months now. the AI field seems to reinvent the wheel every few months. And because most people really don't know what they're talking about, they just jump on the hype and adopt the new so-called standards without really thinking if it's the right approach. It really annoys me because I have been following some open source projects that have had some genuinely novel ideas about AI agent design. And they are mostly ignored by the community. But as soon as a large company like Anthropic or OpenAI starts a trend, suddenly everyone adopts it.
Well, what are those projects? I don't speak for anyone else, but I'm generally fatigued by the endless parade of science fair projects at this point, and operate under the assumption that if an approach is good enough, openai/anthropic/google will fold useful ideas under their tools/products.
Hmm the Gemini API doesn’t need MCP for tool-use if I understand correctly. It just needs registered functions
I don't think any of the mainstream vendor APIs require MCP for tool use - they all supported functions (generally defined using a chunk of OpenAPI JSON schema) before the MCP spec gained widespread acceptance and continue to do so today.
Yep, the Anthropic API supported tool use well before an MCP-related construct was added to the API (MCP connector in May of this year).
While it's not an API, Anthropic's Agent SDK does require MCP to use custom tools.
Yeah, seems like the agent industry is spinning wheels a bit. As that old adage goes, when there are a hundred treatments you can be sure there is no cure.
I can see a perl comeback
Wrapping tool calls in code together with using the benefits of the MCP output schema was implemented in smolagents for some time. Think that’s even one step further conceptually. https://huggingface.co/blog/llchahn/ai-agents-output-schema
What’s the best way to prevent the input context from compounding with each tool call?
Funny how they use "Traditional approach" for MCP tool usage, which was released just a year ago.
This honestly feels like the logical next step for tool calling. Reminds me of the bitter lesson.
Now there's this and "skills", and they're eating each other's lunch.
Very clever. Tool search and “code that can orchestrate tool calls” are features that make utter sense and should become opt out for all tools - not opt in.
How did the industry not think to do this in the first place :)
So basically the idea of Claude Skills just for Tools.
Kinda disappointed, doesn't seem all that advanced to me.
the whole mcp thing is a mess tbh
Tools for tools. How about an LLM tool for tools?
Unfortunate that they chose python instead of bash as the wrapper. Bash would have wider interoperability across languages and workflows that don't touch python. It would also expose more performant tools.
If we're posting opinions, I prefer Python. It's at least as capable as Bash at running external ("more performant") tools.
Not unfortunate. They know what people are using and went that route.
Meanwhile, I have "*Never use Python for anything ever*" in my AGENTS.md.
i think you are leaving lots of intelligence on the table by forbidding python to an LLM; trained heavily on python codebases.
I've mostly stopped using Claude because of it, it will still try use Python for the most random tasks. It recently wrote an HTML file with some inline js in it, then started a local python server to open the HTML file, and check the log output.
This is in a node.js project. It is just too obsessed with using Python, and it seems to help it focus and make more sensible choices by removing the option.