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.

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?

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.

Sorry, isnt this just compression? Lookups burn tokens just on the other end?

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 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).

I thought Codex and Claude Code agents are already token-efficient so writing agents that saves tokens is pointless.

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.

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

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...

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.