{"uid":"cap_6EH4FbbhJWLNjnc6ww2Th","slug":"token-net-supply-flow-sequential-input-change-attribution-99f17765","name":"Token Net Supply Flow — Sequential Input-Change Attribution","description":"Token issued less burned supply flow — Sequential input-change attribution: Sequential input-change attribution for Token issued less burned supply flow. 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-net-supply-flow-waterfall?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"after":{"type":"object","default":{"burned_tokens":440.00000000000006,"issued_tokens":1100},"required":["issued_tokens","burned_tokens"],"properties":{"burned_tokens":{"type":"number","default":400,"maximum":10000000000000000,"minimum":0},"issued_tokens":{"type":"number","default":1000,"maximum":10000000000000000,"minimum":0}},"additionalProperties":false},"before":{"type":"object","default":{"burned_tokens":400,"issued_tokens":1000},"required":["issued_tokens","burned_tokens"],"properties":{"burned_tokens":{"type":"number","default":400,"maximum":10000000000000000,"minimum":0},"issued_tokens":{"type":"number","default":1000,"maximum":10000000000000000,"minimum":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_prQh6Agw4En8eJdsDgWY4","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 a sequential input-change attribution waterfall for token net supply (issued minus burned), comparing before and after token states to explain how each input change contributed to the net supply shift.","exampleAgentPrompt":"Can you run a sequential attribution waterfall on my token net supply change? Before state: 1000 issued tokens and 400 burned; after state: 1100 issued and 440 burned — I want to see how each input change contributed to the shift in net supply.","exampleUseCases":[{"title":"Token burn event impact analysis","prompt":"We just ran a token burn event. Before it, we had 5,000,000 issued tokens and 1,200,000 burned. After, burned tokens rose to 1,800,000 while issued stayed at 5,000,000. Can you do a sequential attribution waterfall to show how the burn contributed to the net supply change?"},{"title":"New token issuance attribution","prompt":"We minted a new batch of tokens. Before: 2,000,000 issued, 500,000 burned. After: 2,350,000 issued, 500,000 burned. Run the input-change attribution to show how the new issuance affected net token supply."},{"title":"Dual-input change decomposition","prompt":"Both our issuance and burn numbers changed this quarter. Starting point was 10,000,000 issued and 3,000,000 burned; ending point is 11,500,000 issued and 3,800,000 burned. I need a sequential waterfall attribution to understand which change — the new issuance or the extra burns — had a bigger impact on net supply."}],"resultDescription":"Returns a deterministic sequential input-change attribution report showing how changes in issued_tokens and burned_tokens between the 'before' and 'after' states each contributed to the overall net supply flow (issued minus burned). The waterfall breaks down the delta step by step, attributing portions of the net supply change to each input variable in sequence. No live market data, safety proof, or trade recommendation is included.","failureModes":["Missing required fields (issued_tokens or burned_tokens in either before or after object) returns a validation error","Values outside the allowed range (0 to 1e16) cause a bounds error","Malformed JSON body returns a parse error","Negative token values are rejected","Payment failure (insufficient USDC or x402 protocol issue) prevents execution"],"whenToPreferThis":"Choose this endpoint when you need a deterministic, reproducible decomposition of how changes in token issuance and burn figures drove a shift in net token supply, particularly when you want to attribute the net supply delta sequentially to each input factor. Prefer it over generic delta calculators when you need an explicit waterfall-style attribution rather than a simple before/after difference, and when you are working with caller-supplied token observations rather than live on-chain data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-13T21:15:12.568Z","isFirstParty":false,"canonicalSlug":"token-net-supply-flow-sequential-input-change-attribution-99f17765"}