{"uid":"cap_r2oUj3bsWgR0RcXzBTeQP","slug":"coinslot-extract-535fba7a","name":"Coinslot Extract","description":"Pull structured data out of raw text: emails, URLs, IPv4/IPv6, phone numbers, dates. Deduplicated with counts. POST { text } (max 500KB).","url":"https://coinslot.dev/api/extract","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string","description":"Raw text, max 500KB"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.003","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":"$0.003/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_QPUylMDBQNA0AMd1VLnww","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.003","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Extracts structured, deduplicated entities (emails, URLs, IPv4/IPv6, phone numbers, dates) from raw text with occurrence counts","exampleAgentPrompt":"Pull out all the emails, phone numbers, URLs, and dates from this raw text and give me a deduplicated list with counts for each: [paste text here]","exampleUseCases":[{"title":"Contact harvesting from email threads","prompt":"I have a long email thread copied as plain text — can you extract every email address and phone number from it, deduplicated, so I can see who's been involved and how many times each appears?"},{"title":"IP address extraction from server logs","prompt":"Here are 50KB of raw server logs — please pull out all unique IPv4 and IPv6 addresses with how many times each one shows up."},{"title":"Date extraction from legal document","prompt":"I've got the text of a contract and I need all the dates mentioned in it pulled out as a clean list so I can review the timeline."}],"resultDescription":"A structured JSON object containing deduplicated arrays for each entity type (emails, URLs, IPv4, IPv6, phone numbers, dates), each entry annotated with an occurrence count indicating how many times it appeared in the input text.","failureModes":["Text exceeds 500KB limit — request rejected with payload-too-large error","Malformed or missing `text` field — 400 bad request","No entities of a given type found — empty array returned for that type (not an error)","Ambiguous phone number formats (e.g. short codes, non-standard separators) may be missed or misclassified","Dates in highly unusual or locale-specific formats may not be recognized"],"whenToPreferThis":"Choose this endpoint when you need to programmatically pull multiple types of structured entities (emails, URLs, IPs, phones, dates) from a single unstructured text blob in one call, with deduplication and counts included automatically. Prefer it over regex-based solutions when you want reliable, battle-tested patterns without writing and maintaining extraction logic yourself. It is especially useful for log analysis, email parsing, document scanning, and data pipeline enrichment tasks where the input is raw text up to 500KB.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T12:32:39.770Z","isFirstParty":false}