{"uid":"cap_mFUlVsOYSMAEjHDt9lmYS","slug":"agentics-structured-data-extractor-e55c4717","name":"Agentics Structured Data Extractor","description":"Correctness-critical services for autonomous AI agents","url":"https://x402rail.net/v1/extract","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"schema":{"type":"object","description":"JSON Schema describing the desired output object. The extractor returns data conforming to this shape; richer property descriptions yield better extraction.","additionalProperties":true},"content":{"type":"string","description":"Raw text, markdown, or HTML to extract from. Capped at the published limit (GET /v1/policy -> limits.extract.maxContentChars); oversized input returns 413 before settlement. Must not contain plaintext PII/secrets (screened, rejected with 422 before the model sees it)."},"instructions":{"type":"string","description":"Optional extra natural-language guidance to steer the extraction (e.g. disambiguation rules)."}}},"responseSchema":{"type":"json","example":{"data":{"title":"Quarterly Report","revenueUsd":1250000,"fiscalQuarter":"Q2-2026"},"model":"claude-sonnet-4-5","tokenUsage":{"input":812,"output":64}}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.05","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"settled","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":"$0.05/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_sXDVbpp-ixEbV30hsnk7S","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.05","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Extracts structured JSON data from raw text, markdown, or HTML according to a caller-supplied JSON Schema","exampleAgentPrompt":"Extract the title, revenue in USD, and fiscal quarter from this text as structured JSON: 'Acme Corp posted $1.25M in revenue for Q2-2026 in their quarterly report.' — use a schema with title (string), revenueUsd (number), and fiscalQuarter (string).","exampleUseCases":null,"resultDescription":"A JSON object with a 'data' field containing the extracted values conforming to the caller-supplied schema, plus the model name used and token usage (input/output counts).","failureModes":["413 returned if content exceeds the character limit from GET /v1/policy before any payment is settled","422 returned if content contains plaintext PII or secrets detected by screening","Malformed or overly vague JSON Schema may yield incomplete or incorrect extraction","Missing required 'schema' or 'content' fields returns a 400-level error","Payment failure or insufficient USDC balance prevents call completion"],"whenToPreferThis":"Choose this endpoint when you need to reliably extract typed, schema-conforming structured data from unstructured or semi-structured text (markdown, HTML, raw prose) in a single call. It is especially well-suited for correctness-critical agent workflows where the output shape must match a predefined contract. Prefer it over general LLM prompting when you want schema enforcement, token usage visibility, and a pay-per-call pricing model via x402.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T12:43:25.095Z","isFirstParty":false}