{"uid":"cap_nUdTkNMGVFYuwqgfCfSU6","slug":"burned-token-fraction-sequential-input-change-attribution-2f4b7651","name":"Burned Token Fraction — Sequential Input-Change Attribution","description":"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.","url":"https://market.datapackvibe.com/x402/assistant-bx-token-burned-supply-waterfall?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"after":{"type":"object","default":{"burned_tokens":110000.00000000001,"issued_tokens":2200000},"required":["burned_tokens","issued_tokens"],"properties":{"burned_tokens":{"type":"number","default":100000,"maximum":10000000000000000,"minimum":0},"issued_tokens":{"type":"number","default":2000000,"maximum":10000000000000000,"minimum":0,"exclusiveMinimum":0}},"additionalProperties":false},"before":{"type":"object","default":{"burned_tokens":100000,"issued_tokens":2000000},"required":["burned_tokens","issued_tokens"],"properties":{"burned_tokens":{"type":"number","default":100000,"maximum":10000000000000000,"minimum":0},"issued_tokens":{"type":"number","default":2000000,"maximum":10000000000000000,"minimum":0,"exclusiveMinimum":0}},"additionalProperties":false}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_kjmWv0yBs2QBrFS5dSUn8","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes how a change in burned or issued token counts affects the reported burned token fraction, using sequential input-change attribution (waterfall analysis).","exampleAgentPrompt":"Run a sequential input-change attribution on the burned token fraction: before we had 100,000 burned tokens out of 2,000,000 issued, and after we have 110,000 burned tokens out of 2,200,000 issued — show me how each input change contributed to the shift in the burn ratio.","exampleUseCases":[{"title":"Token burn event impact analysis","prompt":"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."},{"title":"New token issuance dilution breakdown","prompt":"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."},{"title":"Combined burn and issuance attribution report","prompt":"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."}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:48:11.701Z","isFirstParty":false,"canonicalSlug":"burned-token-fraction-sequential-input-change-attribution-2f4b7651"}