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Token Buy Receipt Shortfall — Sequential Input-Change Attribution
x402
Token Buy Receipt Shortfall — 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 2026-10-12).
Computes a sequential input-change attribution breakdown explaining the shortfall between expected and actual tokens received in a token buy transaction, comparing before and after states.
Price / call$0.01
Success—
StatusUnknown
How this service works
Token buy receipt shortfall — Sequential input-change attribution: Sequential input-change attribution for Token buy receipt shortfall. 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 deterministic, bounded sequential input-change attribution report quantifying how the gap between expected and actual tokens received changed between the 'before' and 'after' input states. The report attributes the shortfall delta across the sequential change in inputs, providing a structured breakdown suitable for auditing or threshold comparison. No live market data, safety guarantees, or trade recommendations are included.
Example use cases
Diagnosing slippage change between two trades
“I made two token purchases and want to understand how the shortfall shifted. In the first buy I expected 1000 tokens but only received 950. In the second buy I expected 1500 tokens but received 1420. Can you run a sequential attribution to explain how the receipt gap changed between those two observations?”
Auditing token buy parameters after a fee change
“Our platform updated its fee structure and I need to see how it affected the token buy shortfall. Before the update: expected 5000 tokens, received 4800. After the update: expected 5000 tokens, received 4950. Run a sequential input-change attribution to show what shifted.”
Comparing shortfall across two different buy sizes
“I want to attribute the difference in token buy shortfall between a small and large purchase. Small buy: expected 200 tokens, got 185. Large buy: expected 10000 tokens, got 9400. Give me a sequential attribution report showing how the shortfall changed across those two input sizes.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-bx-token-buy-receipt-waterfall?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a deterministic, reproducible attribution of token buy receipt shortfall across exactly two sequential input states (before and after). It is ideal for auditing how a change in buy parameters, fees, or slippage settings affected the gap between expected and received tokens. Prefer this over general analytics tools when you need a structured, bounded attribution report rather than a live market scan or trade recommendation.
Side effects
- expected_tokens must be greater than zero (exclusiveMinimum enforced); submitting 0 will cause a validation error
- actual_tokens exceeding the maximum bound (1e16) will be rejected
- missing required fields (expected_tokens or actual_tokens in either before or after objects) will cause a schema error
- mismatched or logically inconsistent inputs (e.g. actual > expected without context) may produce attribution outputs that require careful interpretation
- network or payment failure (x402 micropayment not processed) will prevent the report from being returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| after | object | |
| before | object |
Tags
x402
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