{"uid":"cap_Nr1hVMFwYqF6GsyQVxowh","slug":"api-agent-ia-structured-json-extractor-a72fac7b","name":"api-agent-ia Structured JSON Extractor","description":"Need clean data from messy text? Extracts structured JSON with AI from up to 8000 characters (POST {\"text\"}): a product (name, price, features), an email (sender, recipients, subject, date, summary, actions) or other key fields; keys and generated text in French ($0.01 per call).","url":"https://api.api-agent-ia.ch/extraire-structure?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string"}}},"responseSchema":{"type":"json","example":{"nom":"Chaise de bureau ergonomique ErgoPro 300","prix":249,"type":"produit","caracteristiques":["Dossier en maille respirante","Soutien lombaire réglable","Accoudoirs 4D","Roulettes silencieuses","Garantie 5 ans"]}},"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_-dJodYycM56MJEPNCXDgo","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":"Parses raw unstructured text and returns a structured JSON object with named fields and values extracted from that text","exampleAgentPrompt":"Can you extract the structured data from this text as JSON: 'Red oak chair, price 149 euros, features include solid wood frame, ergonomic backrest, and adjustable height'?","exampleUseCases":[{"title":"Product catalog data extraction","prompt":"I have a bunch of raw product descriptions from a supplier like 'Blue leather sofa, 3 seats, 899 USD, fire resistant and stain proof' — can you pull out the structured fields like name, price, and features as JSON for each one?"},{"title":"Invoice field parsing from raw text","prompt":"Here's some raw text from a scanned invoice: 'Invoice #4821, date 2024-06-15, customer: Acme Corp, total: 3200 USD, items: consulting 2000, hosting 1200' — extract that as structured JSON for me."},{"title":"Form data extraction from user message","prompt":"A user just submitted a support request in plain text: 'My name is Sophie Martin, email sophie@example.com, I ordered product ref XJ-990 on June 3rd and it arrived broken.' Can you parse that into structured JSON fields?"}],"resultDescription":"A JSON object with named fields and values automatically inferred from the input text. Fields, types, and nesting reflect the semantic content of the input — for example, a product description yields fields like name, price, and a list of characteristics. The structure adapts to the content of the provided text.","failureModes":["Empty or very short text may yield an empty JSON object or minimal fields","Highly ambiguous text may produce incorrectly mapped fields","Non-standard languages or domain jargon may reduce extraction accuracy","Malformed or extremely long input strings may cause errors or truncation","If the text contains no structured information, the returned JSON may be nearly empty"],"whenToPreferThis":"Choose this endpoint when you need to convert raw, freeform natural language text into a machine-readable JSON structure without knowing the schema in advance. It is ideal for parsing product descriptions, user-submitted forms, supplier data, or any unstructured text where you need named fields extracted automatically. Prefer it over regex or template-based parsers when the text format is variable or unpredictable.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T12:43:21.740Z","isFirstParty":false,"canonicalSlug":"api-agent-ia-structured-json-extractor-a72fac7b"}