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Why is it replacing true/false with T/F? true/false is already 1 token in all tokenizer I've seen. Even worse is replacing null with ∅. ∅ is a special unicode symbol that takes up 2 tokens compared to the 1 token for null...
Brand new GitHub account, brand new HN account. I stay far away from projects like this these days, they can easily be malicious. GitHub needs some kind of indicator for projects authored by tenured developers with a real identity.
What's even weirder is that the substitution is explained in "How TOON works" section. Yet TOON never describes such behavior anywhere in its spec.
Maybe they want to be tokenizer agnostic? Then they would need to go by character count, right? Despite these inconsistencies, has anyone actually verified their promise? 350 vs. 10.000 tokens would still be very valuable even if they mess up some edge cases
Agreed. This is what happens when you confuse token counts with byte counts.

A trivial test through tiktoken [1] (though technically you really have to match the tokenizer to the specific LLM) would have shown them that ∅ was a poor choice.

Even from the perspective of learned training data, you can probably just intuit that from a frequency standpoint alone the empty-set symbol ∅ can’t possibly have appeared that often outside of things like set theory and logic.

[1] - https://github.com/openai/tiktoken

↲ is also two tokens instead of a simple \n lmao
I don’t think the Show Me section makes sense, the TOON variant clearly doesn’t have the same information. And the examples in the “How TOON works” section focuses on number of characters instead of tokens. I would think “null” is a single token anyway, why bother replacing it with an uncommon character?
Isn't there value to the verbose information too? Knowing what a tool does and what the inputs are increase the likelyhood of successful tool calls.
I spent more than one week, as a side project, to add an MCP server to my Cheméo website. Only 4 tools.

It took me way more time than expected, I was thinking: "Just wrap the REST API, 2h, done".

The MCP payload has nothing to do with the REST API one. Because you need to make it interpretable and context efficient even so it is structured data.

It was really interesting work and I suppose very little people are taking the time to rethink what is sent over the wire while creating a MCP server. If so, we would not have MCPs with the minimal payload being 500kB of JSON soup.

If you send my MCP through your "save token filter", I can guarantee you, that you will have trash down the line.

Yeah this is why a code execution sandbox so the ai can batch calls and select from the response format what it wants and limit the number of responses with instruction to be concise and preserve its context is a really cool thing to do.
This is where using a framework really shines. I used Laravel MCP which makes it trivial to add MCP tools to your CRUD.
I made an MCP proxy with a similar idea in the past: replace a ton of tools that consume tokens with just two (get_tool_schema, invoke_tool) - https://github.com/ameshkov/mcp-compress-router

One thing that I noticed is that it’s often better to return tool names with argument names, i.e. return “search_web(query)” instead of just “search_web” when listing tools. Otherwise models often tend to hallucinate argument names and an extra turn is required to correct the mistake.

One additional advantage that such tools provide is that when you use different coding agents you don’t have to set up all the MCP servers in every agent, you just set up one (or point the agent to the cli like in this project).

How do unresearched, vibe-coded projects like this reach the front page?
I'm convinced that the majority of upvotes are based on reading a title rather than clicking through to an article.

People want a token efficient MCP CLI client. Whether this actually is one is less relevant.

I like to know the average age of accounts which upvoted this post.
Bots, bots everywhere
I was wondering this too, I worked on a project for weeks, posted on HN and got shadowbanned lmao
OP, I'm very interested in seeing an actual comparison ran through a common tokenizer of tool calls. I think you'll find different results than what you intended for this tool to be. You've mixed up tokens with characters on your screen.
Fair point. I used tiktoken (cl100k_base) for all measurements. The 2,034 token count is from the actual JSON tool listing returned by 5 MCP servers (filesystem, memory, sequential-thinking, sqlite, time). The benchmark script is in the repo under /benchmarks if anyone wants to verify.
I think that some of these choices (as others have commented) show that the author has not investigated how tokenization works.

Tokenization is not some black box, you can run tokenizers and check them.

> zero information lost

You're just returning the name of the tool, the rest of the information (description/input schema) is definitely lost. Cut to the LLM making mistakes in calling the tool with incorrect schema or calling the wrong tools altogether, recovering, wasting tokens and cycles.

The format preserves all fields — name, description, and input schema are all there, just encoded with pipes instead of braces and quotes. It's lossless, not a truncation. I should have made that clearer in the post.
I am not going to trust a single number thrown by these AI hustlers written in that salesman voice.

Leave alone 97%.

> Your agent calls 20 tools. Each returns 500-3,000 tokens wrapped in {"content":[{"type":"text","text":"..."}]}.

This is a problem with your tool design. Most MCPs are fully vibe coded without any thought about tool selection.

> On a 128K context window, that's 30-55% gone. Not on work. On syntax.

Tool output is not "syntax" you donkey clanker.

Again, use the code approach, let the LLM filter out the JSON using tools. This TOON thing is just vibes. Most of the time your tool output should not even be JSON. It should be well formatted markdown. In cases where it's large structured data, your LLM should have tools (code / jq) to dissect it. So TOON is pointless.

I like the idea, but this seems a little too aggressive, JSON (287 tokens) — what every other MCP client returns: ~~~ [ {"name": "search_web", "description": "Search the web for information", "inputSchema": {"type": "object", "properties": {"query": {"type": "string", "description": "Search query"}, "num_results": {"type": "number", "default": 5}}, "required": ["query"]}}, {"name": "fetch_url", "description": "Fetch content from a URL", "inputSchema": {"type": "object", "properties": {"url": {"type": "string"}}, "required": ["url"]}} ] TOON (5 tokens) — what mcptoon returns:

search_web fetch_url

~~~

Don’t quite see the point of this. It is well known that MCP is a bit bloated for coding agents at least.

But, why not just use CLIs for each tool? That seems to be where things are going anyway

And using MCP as an internal communication method seems odd when you could use the APIs directly

I really doubt that null and \n make any sense to replace with non ascii symbols. They are both most likely already a token only and for other purposes at least \n becomes larger as a symbol.
How does it work? Im building a video editor and right now it has access to nearly 100 tools. Would be good to learn the techniques you used to make tool discovery more efficient.
sorry, is Headroom still a thing? What happened to it? Is anyone still using it? so many things , which one is actually working :/ idk this ai world
Somewhat related to this project, I'm surprised that not all harnesses are using something like CodeMode for MCPs.

Been experimenting with it in the OpenCode V2 beta and it's pretty great. The combination of tool search, call chaining and field projections feels just right and saves a lot of context. LLMs are good at writing code, who would have thought that?

I thought Codex and Claude Code agents are already token-efficient so writing agents that saves tokens is pointless.
Cool! Can we get a human-efficient MCP CLI while at it? I want to be able to use MCP just as well as the LLMs can.
How is this different from headroom? Mcptoon seems like it's specific to tool calls. https://github.com/headroomlabs-ai/headroom
Q: aren't models trained to message templates using JSON for tool calls. Would a model inherently struggle with a different format?

Q: is there a measurable difference compared to harnesses with tool search?

Sorry, isnt this just compression? Lookups burn tokens just on the other end?
Making MCP context cost visible before the agent sees it feels like a useful debugging tool, not just an optimization.