{"uid":"cap_cfw1mmandmOOu08BkQl69","slug":"foundry-csv-timeseries-gap-evidence-f016e23b","name":"Foundry CSV Timeseries Gap Evidence","description":"Pay-per-request evidence and data tools: CSV validation and reconciliation, DNS/email configuration evidence, and bounded official weather, earthquake, vehicle, company and study records. Exact USDC prices, explicit JSON contracts and source provenance. No mailbox, ownership, medical or safety guarantees.","url":"https://foundry-par007-machine-revenue-mainnet.inference-chip-index.workers.dev/v2/data/csv-timeseries-gap-evidence","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"csv":{"type":"string","maxLength":12000,"minLength":1},"end_time":{"type":"string"},"start_time":{"type":"string"},"step_seconds":{"type":"integer","maximum":86400,"minimum":1},"timestamp_column":{"type":"string","maxLength":100,"minLength":1}}},"responseSchema":{"type":"json","example":{"route":"csv-timeseries-gap-evidence","result":{"status":"GAPS_OR_DUPLICATES","row_count":2,"limitations":["Synthetic example hash."],"input_sha256":"0000000000000000000000000000000000000000000000000000000000000000","missing_count":1,"expected_count":3,"off_grid_count":0,"evidence_truncated":false,"missing_timestamps":["2026-09-06T00:01:00.000Z"],"outside_window_count":0,"invalid_record_numbers":[],"duplicate_aligned_count":0,"invalid_timestamp_count":0,"observed_unique_aligned":2},"observed_at":"2026-09-06T00:00:00Z"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.006","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":"$0.006/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.006","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.006","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_NM-rP5-17OHA7eW6fbHNz","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.006","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Validates a CSV timeseries for missing, duplicate, off-grid, and out-of-window timestamps given an expected time range and step interval.","exampleAgentPrompt":"Check this CSV for any missing or duplicate timestamps — the timestamp column is called 'recorded_at', data should run from 2024-01-01T00:00:00Z to 2024-01-02T00:00:00Z at 60-second intervals. Here's the CSV: [paste CSV text].","exampleUseCases":[{"title":"IoT sensor data completeness audit","prompt":"I have a CSV of temperature readings from our sensor hub — column 'ts', expected every 300 seconds from 2024-06-01T00:00:00Z to 2024-06-07T23:59:00Z. Can you check it for any missing or duplicate timestamps and tell me exactly which ones are absent?"},{"title":"ETL pipeline output verification","prompt":"Our nightly ETL should produce one row per minute from 2024-11-10T00:00:00Z to 2024-11-10T23:59:00Z in the 'event_time' column. Here's the output CSV — validate it for gaps, duplicates, or off-grid entries so I can file a defect report if anything's wrong."},{"title":"Financial trade log reconciliation","prompt":"I need to prove our trade log CSV has complete 15-second interval coverage from 2024-03-15T09:30:00Z to 2024-03-15T16:00:00Z with no missing or duplicate entries — the timestamp field is 'trade_ts'. Can you run the gap evidence check and give me the exact list of any missing timestamps?"}],"resultDescription":"Returns a JSON object with a status field (e.g. GAPS_OR_DUPLICATES or OK), row count, expected count, missing count, list of missing timestamps, duplicate aligned count, off-grid count, outside-window count, invalid timestamp count, observed unique aligned count, whether evidence was truncated, and a SHA-256 hash of the input for provenance. Also includes the route name and observation timestamp.","failureModes":["CSV exceeds 12,000 character maxLength — request rejected","timestamp_column name not found in CSV headers — validation fails or returns invalid counts","start_time or end_time not parseable as ISO timestamps — error response","step_seconds outside 1–86400 range — schema rejection","Malformed CSV with no parseable rows — may return zero counts or error","Payment not included or insufficient USDC — 402 response"],"whenToPreferThis":"Use this endpoint when you need cryptographically provable, pay-per-request evidence of timeseries completeness — specifically when you must identify exact missing timestamps, detect duplicates, and confirm on-grid alignment against a defined schedule. Prefer this over manual scripts when you need an auditable SHA-256 input hash and structured JSON evidence suitable for defect reports, SLA proofs, or pipeline audits. It is a better fit than general-purpose data tools when the input fits within 12,000 characters and you need a deterministic, low-latency answer with explicit source provenance.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T00:53:06.384Z","isFirstParty":false}