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Burned Token Fraction — Sequential Input-Change Attribution
x402
Burned Token Fraction — Sequential Input-Change Attribution is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Computes how a change in burned or issued token counts affects the reported burned token fraction, using sequential input-change attribution (waterfall analysis).
Price / call$0.01
Success—
StatusUnknown
How this service works
Reported burned token fraction — Sequential input-change attribution: Sequential input-change attribution for Reported burned token fraction. Supplied token observations only. Deterministic calculation or caller-threshold verdict, not a live scan, safety proof or trade recommendation. Specialised preset in a shared 50-profile, 20-operation engine. 0.01 USDC includes one bounded sequential input-change attribution report.
Output
A sequential input-change attribution (waterfall) report showing how each input variable (burned_tokens and issued_tokens) individually contributed to the change in the reported burned token fraction between the 'before' and 'after' states. Returns deterministic calculations based on the supplied token observations, not a live scan or trade recommendation.
Example use cases
Token burn event impact analysis
“We just burned 10,000 more tokens — before the burn we had 100,000 burned out of 2,000,000 issued, and after we have 110,000 burned out of 2,000,000 issued. Run a sequential attribution waterfall so I can see how much of the change in the burned fraction came from the burn itself.”
New token issuance dilution breakdown
“Our protocol issued 200,000 new tokens, going from 2,000,000 to 2,200,000 issued, while burned supply stayed at 100,000. Do a before-and-after sequential input-change attribution on the burned token fraction so I can see how much the issuance diluted the burn ratio.”
Combined burn and issuance attribution report
“Both our burned and issued token counts changed: before it was 80,000 burned out of 1,800,000 issued, and after it's 110,000 burned out of 2,200,000 issued. Give me a waterfall attribution showing how each input change — the extra burn and the new issuance — individually contributed to the net change in the burned supply fraction.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-bx-token-burned-supply-waterfall?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to understand how specific changes in burned token count or issued token count each contributed to a shift in the overall burned token fraction — particularly when you want a sequential (waterfall-style) decomposition rather than just a simple before/after delta. It is purpose-built for tokenomics attribution analysis using deterministic calculation on caller-supplied data, with no dependency on live blockchain data.
Side effects
- issued_tokens must be greater than 0 (exclusiveMinimum enforced); submitting 0 will return a validation error
- burned_tokens exceeding issued_tokens may produce a fraction greater than 1, which may be flagged or rejected
- missing required fields (burned_tokens or issued_tokens in either before or after objects) will cause a schema validation error
- values exceeding the maximum of 10^16 will be rejected
- payment failure or insufficient USDC balance will result in a 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| after | object | |
| before | object |
Tags
x402
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