๐ Just released TOON Context MCP Server!
Transparent token optimization for coding agents - converts JSON/CSV to 30-60% more efficient format automatically. No workflow changes needed, just 50% less tokens! ๐งต @kilocode@cline@GithubProjects#AI#MCP#Tokens#Coding
A simple trick cuts your LLM costs by 50%!
Just stop using JSON and use this instead:
TOON (Token-Oriented Object Notation) slashes your LLM token usage in half while keeping data perfectly readable.
Here's why it works:
TOON's sweet spot: uniform arrays with consistent fields per row. It merges YAML's indentation and CSV's tabular structure, optimized for minimal tokens.
Look at the example below.
JSON:
{
"๐๐๐ฒ๐ฟ๐": [
{ "๐ถ๐ฑ": ๐ญ, "๐ป๐ฎ๐บ๐ฒ": "๐๐น๐ถ๐ฐ๐ฒ", "๐ฟ๐ผ๐น๐ฒ": "๐ฎ๐ฑ๐บ๐ถ๐ป" },
{ "๐ถ๐ฑ": ๐ฎ, "๐ป๐ฎ๐บ๐ฒ": "๐๐ผ๐ฏ", "๐ฟ๐ผ๐น๐ฒ": "๐๐๐ฒ๐ฟ" }
]
}
Toon:
๐๐๐ฒ๐ฟ๐[๐ฎ]{๐ถ๐ฑ,๐ป๐ฎ๐บ๐ฒ,๐ฟ๐ผ๐น๐ฒ}:
๐ญ,๐๐น๐ถ๐ฐ๐ฒ,๐ฎ๐ฑ๐บ๐ถ๐ป
๐ฎ,๐๐ผ๐ฏ,๐๐๐ฒ๏ฟฝ๏ฟฝ๏ฟฝ๏ฟฝ
It's obvious how few tokens are being used to represent the same information!
To summarise, here are the key features:
๐ธ 30โ60% fewer tokens than JSON
๐ Borrows the best from YAML & CSV
๐คฟ Built-in validation with explicit lengths & fields
๐ฑ Minimal syntax (no redundant braces, brackets, etc.)
IMPORTANT!!
That said, for deeply nested or non-uniform data, JSON might be more efficient.
In the next tweet, I've shared some benchmark results demonstrating the effectiveness of this technique in reducing the number of tokens and improving retrieval accuracy with popular LLM providers.
Where do you think this could be effective in your existing workflows?
Find the relevant links in the next tweet!
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Crazy how much more informed you can be by just reading the README file on the repo.
Armin is wrong here, the training data doesn't matter. TOON makes most models more accurate for lookup.
@natsukashixo@peach2k2 Exactly, mcps like mine that use toon or convert it to another human-readable format, it's the best way to optimize data input for now. Deepseek is working on just using images for context tho.
๐ Just released TOON Context MCP Server!
Transparent token optimization for coding agents - converts JSON/CSV to 30-60% more efficient format automatically. No workflow changes needed, just 50% less tokens! ๐งต @kilocode@cline@GithubProjects#AI#MCP#Tokens#Coding
๐ Just released TOON Context MCP Server!
Transparent token optimization for coding agents - converts JSON/CSV to 30-60% more efficient format automatically. No workflow changes needed, just 50% less tokens! ๐งต @FactoryAI@cline@claudeai@AnthropicAI#AI#MCP#Tokens#Coding
๐ Just released TOON Context MCP Server!
Transparent token optimization for coding agents - converts JSON/CSV to 30-60% more efficient format automatically. No workflow changes needed, just 50% less tokens! ๐งต @kilocode@cline@GithubProjects#AI#MCP#Tokens#Coding
Bold prediction for 2026:
JavaScript will still be "dying" Python will still be "too slow" Rust will still be "the future" And PHP will still be running 70% of the internet
Tech Twitter predictions are entertainment, not strategy.
Build with what works. Ship fast. Make money.
The tech stack wars are won by those who ignore them.
@AnthropicAI This is why it's kinda silly when people glaze Chinese models so much, the reason why they can only spend 6 million on training (they actually just fine tune) is because I'm sure the intellectual theft rate is extremely high like other sectors.