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StationFX JSON-to-TOON Converter
x402
StationFX JSON-to-TOON Converter is a paid API for AI agents from stationfx.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-01).
Converts a JSON payload into TOON (Tokenized Object Notation), a compact pipe-delimited format that reduces token count by ~60-70% versus equivalent JSON for time-series data.
Price / call$0.005
Success—
StatusUnknown
How this service works
Convert a JSON payload to TOON (Tokenized Object Notation), a compact pipe-delimited format that achieves ~60-70% token reduction versus equivalent JSON on time-series payloads. Accepts a raw array or an envelope {meta, data, result_type}. Max body: 512 KB (HTTP 413 if exceeded; checked before payment). Pay $0.005 per call via x402 USDC on Base, or use a bearer token from /token/purchase.
Output
A plain-text TOON-formatted string (text/plain; charset=utf-8) consisting of pipe-delimited rows with a header block, representing the original JSON data in a compact form that typically uses 60-70% fewer tokens than the equivalent JSON for time-series payloads.
Example use cases
Compressing financial data for LLM analysis
“I have a big JSON payload of daily stock price data — about 300 KB — and I need to squeeze it down before sending it to GPT-4. Can you convert it to TOON format to cut the token count?”
Optimizing macro data feeds for AI pipelines
“I'm pulling monthly CPI and unemployment data as JSON from an economic API and it's eating up my context window. Convert this JSON envelope with the meta and data fields to TOON so I can process more history at once.”
Reducing token cost in agent memory
“My agent keeps hitting context limits when it stores time-series sensor readings in JSON. Convert this raw JSON array of readings to TOON pipe-delimited format so it takes up far fewer tokens in memory.”
Technical details
Endpoint
POSThttps://stationfx.com/util/convert/json-to-toon?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to pass large JSON time-series datasets to an LLM and context window or token cost is a concern. It is specifically optimized for array-shaped time-series data (e.g. financial, economic, or sensor readings) and achieves 60-70% token reduction. Prefer it over generic compression if your goal is LLM-token efficiency rather than byte-level compression. Not ideal for deeply nested or non-tabular JSON structures where the gains may be smaller.
Side effects
- HTTP 413 if the request body exceeds 512 KB — checked before payment is processed
- Invalid or malformed JSON body returns an error without consuming a payment
- Missing required 'input' field in the request body results in a validation error
- Unsupported HTTP method (anything other than POST/PUT/PATCH) returns a 405
- Authentication failure if bearer token is invalid or expired; payment via x402 USDC on Base required if no token is present
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"schema": {
"type": "object",
"required": [
"result"
],
"properties": {
"result": {
"type": "string",
"description": "TOON-formatted text (text/plain; charset=utf-8). Pipe-delimited rows: result_type header, field names, separator, then one row per observation. ~60-70% fewer tokens than equivalent JSON for time-series data."
},
"content_type": {
"type": "string",
"description": "Always text/plain; charset=utf-8"
}
}
},
"example": {
"result": "result_type=series\nfields=date|value\n---\n2024-01-01|27360.99\n2024-04-01|27490.12",
"content_type": "text/plain; charset=utf-8"
}
}Tags
x402
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