{"uid":"cap_wK1qqHwp32yL8Ny0FeZUT","slug":"pocket-network-structured-extraction-1ece7c19","name":"Pocket Network Structured Extraction","description":"Rule-based extraction of named fields from text; not a language model, it returns only what the text states. POST /v1/extract with {text, schema}: schema is a JSON Schema, a list of field names, or {fields: {name: regex}}. Built-in fields include email, phone, date, url, money and company; other fields come from \"Label: value\" lines. Pay per request in USDC; no account, no API key.","url":"https://agent.pocket.network/v1/structured-extraction?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string","description":"The text to extract from."},"schema":{"description":"A JSON Schema with properties, a list of field names, or {fields: {name: regex}}."}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.005","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":"$0.005/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_hWqEa0ELPDGmR_BkMAAqU","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.005","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Extracts named fields from unstructured text using rule-based pattern matching and JSON Schema definitions, returning only what the text explicitly states.","exampleAgentPrompt":"Pull the email, phone number, company name, and any dates from this text: 'Please contact Sarah at sarah@acme.com or call 555-234-5678. Acme Corp is hosting a kickoff on March 15, 2025.'","exampleUseCases":[{"title":"Contact info from email body","prompt":"Extract the sender's email address, phone number, and company name from this email body: 'Hi, I'm Tom from BrightPath Inc. Reach me at tom@brightpath.com or 212-555-9090 for any follow-up.'"},{"title":"Invoice field parsing","prompt":"From this invoice text, extract the invoice date, total amount, and any URLs mentioned: 'Invoice #4421 dated 2024-11-01. Total due: $3,450.00. Pay at https://pay.vendor.com/inv4421.'"},{"title":"Custom regex field extraction","prompt":"Parse this support ticket and extract the fields 'ticket_id', 'priority', and 'customer_email' — priority lines look like 'Priority: High' — from the text: 'Ticket-ID: 9821\\nPriority: High\\nCustomer-Email: joe@example.com\\nIssue: Login not working.'"}],"resultDescription":"A JSON object mapping each requested field name to the value extracted from the text, using only content explicitly present in the input. Built-in fields (email, phone, date, url, money, company) are recognized automatically; custom fields match 'Label: value' lines or user-supplied regex patterns. Fields not found in the text are omitted or returned as null.","failureModes":["Field not found in text — field is omitted or null if the pattern or label is absent","Ambiguous or malformed schema — returns error if the schema argument cannot be interpreted","Text too short or lacks structure — built-in heuristics may miss values in highly unstructured prose","Regex pattern mismatch — custom regex fields return nothing if no substring matches","Payment failure — request rejected if USDC payment is not included or insufficient"],"whenToPreferThis":"Choose this endpoint when you need deterministic, rule-based field extraction from text and do not want an LLM to infer or hallucinate values — it returns only what the text explicitly states. Ideal for parsing contact info, invoices, receipts, or structured documents where field labels follow consistent patterns. Prefer it over LLM-based extraction when auditability, cost predictability, and strict literal fidelity matter more than semantic understanding.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-01T12:30:11.391Z","isFirstParty":false,"canonicalSlug":"pocket-network-structured-extraction-1ece7c19"}